ACR_Training/Bachelor Research - ACR.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"accelerator":"GPU","colab":{"name":"Bachelor Research - ACR.ipynb","provenance":[],"collapsed_sections":["2ESDjHx946Jr","YEbigvdHZCrS","Gc7Y5RBuY-p9","gwHmCGIUVz2D","TeCtfXyuT5t2","qR68vnSUTb6j","gkdSbh6CUKn6","KoT88AF7sVcs","InPjy1YPS_Ay","swL8rxfAibTq","iOer3ByMMei-","LfujmiVa2GQo","fX34Xs1gNLfF","TeMBC0TF35iq","t-Eb-u76vHgt","-mYkFhTOvh-n"],"mount_file_id":"1wG4PbRSKCJ2BmbhMqyMtLgmodzr2tc-m","authorship_tag":"ABX9TyMYkI3/HHOuRgqB2Ps/8bxL"},"kernelspec":{"display_name":"Python 3","name":"python3"}},"cells":[{"cell_type":"markdown","metadata":{"id":"2ESDjHx946Jr"},"source":["## Installations and Imports :\n","\n"]},{"cell_type":"code","metadata":{"id":"C1UdjUfD3j-7"},"source":["!pip install sklearn librosa mir_eval tensorflow"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"5xfbSjrYxvCO"},"source":["#!/usr/bin/env python3\n","import argparse\n","\n","from numpy.lib.npyio import save\n","from ACR_Training.Models import MLP, MLP_scalered, CRNN, CRNN_1, MLP2RNN, CRNN_2, BassVsThird\n","from ACR_Training.Datasets import IsophonicsDataset \n","import sklearn\n","import sys"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"JuTSQ3fN5s0g"},"source":["#!/usr/bin/env python3\n","parser = argparse.ArgumentParser()\n","# Directories, destinations, folders, files\n","parser.add_argument(\"--isophonics_audio_directory\", default=\"./Datasets/Isophonics/AUDIO\", type=str, help=\"Path to ISOPHONICS directory with audio files.\")\n","parser.add_argument(\"--isophonics_annotations_directory\", default=\"./Datasets/Isophonics/ANNOTATIONS\", type=str, help=\"Path to ISOPHONICS directory with chord annotations.\")\n","parser.add_argument(\"--billboard_audio_directory\", default=\"./Datasets/Billboard/AUDIO\", type=str, help=\"Path to BILLBOARD directory with audio files.\")\n","parser.add_argument(\"--billboard_annotations_directory\", default=\"./Datasets/Billboard/ANNOTATIONS\", type=str, help=\"Path to BILLBOARD directory with chord annotations.\")\n","parser.add_argument(\"--isophonics_prep_dest\", default=\"./PreprocessedDatasets/isophonics_new.ds\", type=str, help=\"Preprocessed ISOPHONICS dataset destination.\")\n","parser.add_argument(\"--billboard_prep_dest\", default=\"./PreprocessedDatasets/billboard_new.ds\", type=str, help=\"Preprocessed BILLBOARD dataset destination.\")\n","\n","# Dataset preprocessing args\n","parser.add_argument(\"--dataset\", default=\"isophonics\", type=str, help=\"Dataset we want to preprocess, {isophonics, billboard}\")\n","#           Isophonics\n","parser.add_argument(\"--sample_rate\", default=44100, type=int, help=\"Sample rate for each song.\")\n","parser.add_argument(\"--hop_length\", default=512, type=int, help=\"10*(sample_rate/hop_length) is a number of miliseconds between two frames.\")\n","parser.add_argument(\"--window_size\", default=8, type=int, help=\"Spectrograms on left, and also spectrogram on right of the time bin -> window_size*2 + 1 spectrograms grouped together.\")\n","parser.add_argument(\"--flattened_window\", default=False, type=bool, help=\"Whether the spectrogram window should be flatten to one array or it sould be array of spectrograms.\")\n","parser.add_argument(\"--ms_intervals\", default=430.6640625, type=float, help=\"Miliseconds between generated spectrograms.\")\n","parser.add_argument(\"--to_skip\", default=10, type=int, help=\"How many spectrogram we want to skip when creating spectrogram window.\")\n","parser.add_argument(\"--norm_to_C\", default=True, type=bool, help=\"Whether we want to transpose all songs to C key (or D dorian, .. A minor, ...)\")\n","parser.add_argument(\"--spectrogram_type\", default=\"cqt\", type=str, help=\"Spectrogram types, {cqt,log_mel}\")\n","#           Billboard\n","parser.add_argument(\"--n_frames\", default=1000, type=int, help=\"Length of song subsequence we are consinder when predicting chords to keep some context.\")\n","\n","# Training args\n","parser.add_argument(\"--test_size\", default=0.3, type=lambda x:int(x) if x.isdigit() else float(x), help=\"Test set size.\")\n","parser.add_argument(\"--epochs\", default=100, type=int, help=\"Number of epochs.\")\n","parser.add_argument(\"--seed\", default=42, type=int, help=\"Random seed.\")\n","\n","\n","args = parser.parse_args([] if \"__file__\" not in globals() else None)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"IBqhmTkDuklY"},"source":["# ISOPHONICS DATASET \n","*STFT length is by default 2^14 = 16384*"]},{"cell_type":"markdown","metadata":{"id":"xi-lAUdajy2U"},"source":["## MLP, scaler preprocessing\n","\n","*Probably not that accurate because the dev dataset is created from randomly choosed spectrograms. There could be very similar samples as in the train dataset.*"]},{"cell_type":"markdown","metadata":{"id":"YEbigvdHZCrS"},"source":["### All songs in C major key - *83.02%*"]},{"cell_type":"code","metadata":{"id":"dXbdNUogYKNN","colab":{"base_uri":"https://localhost:8080/","height":459},"executionInfo":{"status":"ok","timestamp":1616880219190,"user_tz":-60,"elapsed":1104,"user":{"displayName":"Vojtech Lanz","photoUrl":"","userId":"15791523486540207166"}},"outputId":"234c2bab-44ea-4943-c2d5-3ba52460a8bf"},"source":["# sample_rate = 44100, hop_length = 1024, window_size = 5, skip_coef = 22, norm_to_C = True, feature_type = log_mel_spec, flattened_window = True\n","# Prepare data\n","x, y = IsophonicsDataset.load_preprocessed_dataset(\"./ACR_Training/PreprocessedDatasets/isophonics_MLP_inflated_transposed.ds\")\n","train_x, dev_x, train_y, dev_y = sklearn.model_selection.train_test_split(x, y, test_size=0.3, random_state=42)\n","\n","# Create model\n","mlp_model = MLP_scalered(random_state=7)\n","\n","# Train model\n","mlp_model.fit(train_x, train_y)\n","\n","# Display score\n","print(\"\\n\",\"Dev accuracy: \", \"{:.2f}\".format(100*mlp_model.score(dev_x, dev_y)), \"%\", \"\\n\")\n","mlp_model.display_confusion_matrix(dev_x, dev_y)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["[INFO] The Preprocessed Isophonics Dataset was loaded successfully.\n","[INFO] The MLP model with scaler preprocessing was successfully created.\n","[INFO] The MLP model was successfully trained.\n","\n"," Dev accuracy:  83.02 % \n","\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x432 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"Gc7Y5RBuY-p9"},"source":["### MLP, All songs in original key - *82.59%* "]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":457},"id":"YnA69X9WWpp_","executionInfo":{"status":"ok","timestamp":1616709184679,"user_tz":-60,"elapsed":892,"user":{"displayName":"Vojtech Lanz","photoUrl":"","userId":"15791523486540207166"}},"outputId":"41b6ac2b-5084-4e54-8aee-1e1ffbafa892"},"source":["# sample_rate = 44100, hop_length = 1024, window_size = 5, skip_coef = 22, norm_to_C = False, feature_type = log_mel_spec, flattened_window = True\n","# Prepare data\n","x, y = IsophonicsDataset.load_preprocessed_dataset(\"./ACR_Training/PreprocessedDatasets/isophonics_MLP_inflated_original.ds\")\n","train_x, dev_x, train_y, dev_y = sklearn.model_selection.train_test_split(x, y, test_size=0.3, random_state=42)\n","\n","# Create model\n","mlp_model = MLP_scalered(random_state=7)\n","\n","# Train model\n","mlp_model.fit(train_x, train_y)\n","\n","# Display score\n","print(\"\\n\",\"Dev accuracy: \", \"{:.2f}\".format(100*mlp_model.score(dev_x, dev_y)), \"%\", \"\\n\")\n","mlp_model.display_confusion_matrix(dev_x, dev_y)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["[INFO] The preprocessed Isophonics Dataset was loaded successfully.\n","[INFO] The MLP model with scaler preprocessing was successfully created.\n","[INFO] The MLP model was successfully trained.\n","\n"," Dev accuracy:  82.59 % \n","\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x432 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"gwHmCGIUVz2D"},"source":["## CRNN model\n","*sample rate 22050, hop_length 512, 1000 frames*"]},{"cell_type":"markdown","metadata":{"id":"TeCtfXyuT5t2"},"source":["### *shifted CQT - 68.12%*"]},{"cell_type":"code","metadata":{"trusted":true,"id":"a0N687FUTz1B","outputId":"db44c06f-8378-4d12-8c3e-4daaf5c3179d"},"source":["#!/usr/bin/env python3\n","# Prepare data\n","x, y = IsophonicsDataset.load_preprocessed_dataset(\"../input/acrisophonics2/Isophonics1000.ds\")\n","n_samples, n_frames, n_chromas = x.shape\n","x = x.reshape((n_samples,n_frames,n_chromas,1))\n","train_x, dev_x, train_y, dev_y = sklearn.model_selection.train_test_split(x, y, test_size=0.3, random_state=args.seed)\n"," \n","# Create model\n","crnn_model = CRNN_1(input_shape=(n_frames, n_chromas, 1), output_classes=25)\n"," \n","# Train model\n","crnn_model.fit(train_x, train_y, dev_x, dev_y, epochs=75)\n"," \n","# Display score\n","crnn_model.display_training_progress()\n","print(\"\\n\",\"Dev accuracy: \", \"{:.2f}\".format(100*crnn_model.score(dev_x, dev_y)), \"%\", \"\\n\")\n","crnn_model.display_confusion_matrix(dev_x, dev_y)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["[INFO] The Preprocessed Isophonics Dataset was loaded successfully.\n","Model: \"sequential\"\n","_________________________________________________________________\n","Layer (type)                 Output Shape              Param #   \n","=================================================================\n","conv2d (Conv2D)              (None, 1000, 252, 16)     160       \n","_________________________________________________________________\n","batch_normalization (BatchNo (None, 1000, 252, 16)     64        \n","_________________________________________________________________\n","conv2d_1 (Conv2D)            (None, 1000, 252, 16)     2320      \n","_________________________________________________________________\n","batch_normalization_1 (Batch (None, 1000, 252, 16)     64        \n","_________________________________________________________________\n","conv2d_2 (Conv2D)            (None, 1000, 252, 16)     2320      \n","_________________________________________________________________\n","batch_normalization_2 (Batch (None, 1000, 252, 16)     64        \n","_________________________________________________________________\n","max_pooling2d (MaxPooling2D) (None, 1000, 84, 16)      0         \n","_________________________________________________________________\n","conv2d_3 (Conv2D)            (None, 1000, 84, 32)      4640      \n","_________________________________________________________________\n","batch_normalization_3 (Batch (None, 1000, 84, 32)      128       \n","_________________________________________________________________\n","conv2d_4 (Conv2D)            (None, 1000, 84, 32)      9248      \n","_________________________________________________________________\n","batch_normalization_4 (Batch (None, 1000, 84, 32)      128       \n","_________________________________________________________________\n","conv2d_5 (Conv2D)            (None, 1000, 84, 32)      9248      \n","_________________________________________________________________\n","batch_normalization_5 (Batch (None, 1000, 84, 32)      128       \n","_________________________________________________________________\n","max_pooling2d_1 (MaxPooling2 (None, 1000, 28, 32)      0         \n","_________________________________________________________________\n","conv2d_6 (Conv2D)            (None, 1000, 28, 64)      18496     \n","_________________________________________________________________\n","batch_normalization_6 (Batch (None, 1000, 28, 64)      256       \n","_________________________________________________________________\n","conv2d_7 (Conv2D)            (None, 1000, 28, 64)      36928     \n","_________________________________________________________________\n","batch_normalization_7 (Batch (None, 1000, 28, 64)      256       \n","_________________________________________________________________\n","max_pooling2d_2 (MaxPooling2 (None, 1000, 7, 64)       0         \n","_________________________________________________________________\n","conv2d_8 (Conv2D)            (None, 1000, 7, 80)       46160     \n","_________________________________________________________________\n","batch_normalization_8 (Batch (None, 1000, 7, 80)       320       \n","_________________________________________________________________\n","conv2d_9 (Conv2D)            (None, 1000, 7, 80)       57680     \n","_________________________________________________________________\n","batch_normalization_9 (Batch (None, 1000, 7, 80)       320       \n","_________________________________________________________________\n","reshape (Reshape)            (None, 1000, 560)         0         \n","_________________________________________________________________\n","bidirectional (Bidirectional (None, 1000, 192)         504576    \n","_________________________________________________________________\n","dense (Dense)                (None, 1000, 25)          4825      \n","=================================================================\n","Total params: 698,329\n","Trainable params: 697,465\n","Non-trainable params: 864\n","_________________________________________________________________\n","[INFO] The CRNN model was successfully created.\n","Epoch 1/75\n","40/40 [==============================] - 33s 617ms/step - loss: 2.4952 - accuracy: 0.2601 - val_loss: 3.3328 - val_accuracy: 0.1507\n","Epoch 2/75\n","40/40 [==============================] - 22s 539ms/step - loss: 2.2048 - accuracy: 0.3213 - val_loss: 3.3264 - val_accuracy: 0.1507\n","Epoch 3/75\n","40/40 [==============================] - 22s 540ms/step - loss: 2.1659 - accuracy: 0.3270 - val_loss: 4.4865 - val_accuracy: 0.1507\n","Epoch 4/75\n","40/40 [==============================] - 22s 541ms/step - loss: 2.1441 - accuracy: 0.3334 - val_loss: 3.5667 - val_accuracy: 0.1507\n","Epoch 5/75\n","40/40 [==============================] - 22s 539ms/step - loss: 2.0922 - accuracy: 0.3485 - val_loss: 2.5361 - val_accuracy: 0.1651\n","Epoch 6/75\n","40/40 [==============================] - 22s 539ms/step - loss: 1.9520 - accuracy: 0.4005 - val_loss: 2.2939 - val_accuracy: 0.2709\n","Epoch 7/75\n","40/40 [==============================] - 22s 541ms/step - loss: 1.8042 - accuracy: 0.4478 - val_loss: 2.7215 - val_accuracy: 0.1800\n","Epoch 8/75\n","40/40 [==============================] - 22s 541ms/step - loss: 1.7316 - accuracy: 0.4755 - val_loss: 2.4425 - val_accuracy: 0.2703\n","Epoch 9/75\n","40/40 [==============================] - 22s 541ms/step - loss: 1.5673 - accuracy: 0.5288 - val_loss: 2.1774 - val_accuracy: 0.3438\n","Epoch 10/75\n","40/40 [==============================] - 22s 540ms/step - loss: 1.4781 - accuracy: 0.5544 - val_loss: 1.9907 - val_accuracy: 0.4202\n","Epoch 11/75\n","40/40 [==============================] - 22s 540ms/step - loss: 1.3499 - accuracy: 0.5819 - val_loss: 1.7041 - val_accuracy: 0.4986\n","Epoch 12/75\n","40/40 [==============================] - 22s 540ms/step - loss: 1.3081 - accuracy: 0.5985 - val_loss: 1.5148 - val_accuracy: 0.5338\n","Epoch 13/75\n","40/40 [==============================] - 21s 539ms/step - loss: 1.1980 - accuracy: 0.6263 - val_loss: 1.4425 - val_accuracy: 0.5602\n","Epoch 14/75\n","40/40 [==============================] - 22s 541ms/step - loss: 1.1163 - accuracy: 0.6519 - val_loss: 1.3883 - val_accuracy: 0.5781\n","Epoch 15/75\n","40/40 [==============================] - 22s 540ms/step - loss: 1.0463 - accuracy: 0.6720 - val_loss: 1.3393 - val_accuracy: 0.5904\n","Epoch 16/75\n","40/40 [==============================] - 22s 541ms/step - loss: 0.9900 - accuracy: 0.6911 - val_loss: 1.2852 - val_accuracy: 0.6073\n","Epoch 17/75\n","40/40 [==============================] - 22s 539ms/step - loss: 0.9125 - accuracy: 0.7149 - val_loss: 1.2766 - val_accuracy: 0.6119\n","Epoch 18/75\n","40/40 [==============================] - 22s 542ms/step - loss: 0.8428 - accuracy: 0.7383 - val_loss: 1.2434 - val_accuracy: 0.6198\n","Epoch 19/75\n","40/40 [==============================] - 22s 539ms/step - loss: 0.7928 - accuracy: 0.7560 - val_loss: 1.3460 - val_accuracy: 0.6027\n","Epoch 20/75\n","40/40 [==============================] - 22s 542ms/step - loss: 0.7138 - accuracy: 0.7743 - val_loss: 1.2172 - val_accuracy: 0.6318\n","Epoch 21/75\n","40/40 [==============================] - 21s 538ms/step - loss: 0.6649 - accuracy: 0.7907 - val_loss: 1.2287 - val_accuracy: 0.6317\n","Epoch 22/75\n","40/40 [==============================] - 22s 539ms/step - loss: 0.6093 - accuracy: 0.8077 - val_loss: 1.2000 - val_accuracy: 0.6408\n","Epoch 23/75\n","40/40 [==============================] - 22s 540ms/step - loss: 0.5887 - accuracy: 0.8147 - val_loss: 1.2264 - val_accuracy: 0.6403\n","Epoch 24/75\n","40/40 [==============================] - 22s 540ms/step - loss: 0.5262 - accuracy: 0.8367 - val_loss: 1.2584 - val_accuracy: 0.6134\n","Epoch 25/75\n","40/40 [==============================] - 22s 539ms/step - loss: 0.4844 - accuracy: 0.8485 - val_loss: 1.1958 - val_accuracy: 0.6483\n","Epoch 26/75\n","40/40 [==============================] - 22s 540ms/step - loss: 0.4578 - accuracy: 0.8589 - val_loss: 1.2029 - val_accuracy: 0.6464\n","Epoch 27/75\n","40/40 [==============================] - 22s 542ms/step - loss: 0.4361 - accuracy: 0.8649 - val_loss: 1.2386 - val_accuracy: 0.6432\n","Epoch 28/75\n","40/40 [==============================] - 22s 540ms/step - loss: 0.4182 - accuracy: 0.8726 - val_loss: 1.2000 - val_accuracy: 0.6532\n","Epoch 29/75\n","40/40 [==============================] - 22s 540ms/step - loss: 0.4102 - accuracy: 0.8717 - val_loss: 1.2262 - val_accuracy: 0.6544\n","Epoch 30/75\n","40/40 [==============================] - 21s 538ms/step - loss: 0.3573 - accuracy: 0.8897 - val_loss: 1.2025 - val_accuracy: 0.6588\n"],"name":"stdout"},{"output_type":"stream","text":["Epoch 31/75\n","40/40 [==============================] - 22s 539ms/step - loss: 0.3169 - accuracy: 0.9026 - val_loss: 1.1944 - val_accuracy: 0.6661\n","Epoch 32/75\n","40/40 [==============================] - 21s 539ms/step - loss: 0.2845 - accuracy: 0.9122 - val_loss: 1.2112 - val_accuracy: 0.6569\n","Epoch 33/75\n","40/40 [==============================] - 22s 539ms/step - loss: 0.2645 - accuracy: 0.9195 - val_loss: 1.2178 - val_accuracy: 0.6660\n","Epoch 34/75\n","40/40 [==============================] - 21s 539ms/step - loss: 0.2411 - accuracy: 0.9268 - val_loss: 1.2136 - val_accuracy: 0.6694\n","Epoch 35/75\n","40/40 [==============================] - 22s 542ms/step - loss: 0.2307 - accuracy: 0.9309 - val_loss: 1.2414 - val_accuracy: 0.6645\n","Epoch 36/75\n","40/40 [==============================] - 22s 540ms/step - loss: 0.2081 - accuracy: 0.9374 - val_loss: 1.2404 - val_accuracy: 0.6697\n","Epoch 37/75\n","40/40 [==============================] - 22s 542ms/step - loss: 0.1857 - accuracy: 0.9448 - val_loss: 1.2491 - val_accuracy: 0.6699\n","Epoch 38/75\n","40/40 [==============================] - 22s 540ms/step - loss: 0.1843 - accuracy: 0.9449 - val_loss: 1.2442 - val_accuracy: 0.6713\n","Epoch 39/75\n","40/40 [==============================] - 22s 539ms/step - loss: 0.1613 - accuracy: 0.9522 - val_loss: 1.2648 - val_accuracy: 0.6694\n","Epoch 40/75\n","40/40 [==============================] - 22s 540ms/step - loss: 0.1594 - accuracy: 0.9527 - val_loss: 1.2768 - val_accuracy: 0.6663\n","Epoch 41/75\n","40/40 [==============================] - 22s 542ms/step - loss: 0.1524 - accuracy: 0.9545 - val_loss: 1.2721 - val_accuracy: 0.6732\n","Epoch 42/75\n","40/40 [==============================] - 22s 541ms/step - loss: 0.1354 - accuracy: 0.9595 - val_loss: 1.2956 - val_accuracy: 0.6712\n","Epoch 43/75\n","40/40 [==============================] - 21s 539ms/step - loss: 0.1314 - accuracy: 0.9610 - val_loss: 1.3085 - val_accuracy: 0.6706\n","Epoch 44/75\n","40/40 [==============================] - 22s 540ms/step - loss: 0.1315 - accuracy: 0.9605 - val_loss: 1.2998 - val_accuracy: 0.6770\n","Epoch 45/75\n","40/40 [==============================] - 22s 540ms/step - loss: 0.1224 - accuracy: 0.9632 - val_loss: 1.3057 - val_accuracy: 0.6728\n","Epoch 46/75\n","40/40 [==============================] - 21s 538ms/step - loss: 0.1121 - accuracy: 0.9661 - val_loss: 1.3098 - val_accuracy: 0.6729\n","Epoch 47/75\n","40/40 [==============================] - 22s 539ms/step - loss: 0.1094 - accuracy: 0.9675 - val_loss: 1.3434 - val_accuracy: 0.6742\n","Epoch 48/75\n","40/40 [==============================] - 22s 540ms/step - loss: 0.1018 - accuracy: 0.9699 - val_loss: 1.3369 - val_accuracy: 0.6738\n","Epoch 49/75\n","40/40 [==============================] - 22s 539ms/step - loss: 0.0973 - accuracy: 0.9706 - val_loss: 1.3282 - val_accuracy: 0.6759\n","Epoch 50/75\n","40/40 [==============================] - 22s 542ms/step - loss: 0.0999 - accuracy: 0.9692 - val_loss: 1.3454 - val_accuracy: 0.6771\n","Epoch 51/75\n","40/40 [==============================] - 22s 541ms/step - loss: 0.0956 - accuracy: 0.9708 - val_loss: 1.3524 - val_accuracy: 0.6773\n","Epoch 52/75\n","40/40 [==============================] - 22s 540ms/step - loss: 0.0898 - accuracy: 0.9728 - val_loss: 1.3595 - val_accuracy: 0.6781\n","Epoch 53/75\n","40/40 [==============================] - 22s 543ms/step - loss: 0.0831 - accuracy: 0.9751 - val_loss: 1.3832 - val_accuracy: 0.6776\n","Epoch 54/75\n","40/40 [==============================] - 22s 539ms/step - loss: 0.0812 - accuracy: 0.9751 - val_loss: 1.4162 - val_accuracy: 0.6770\n","Epoch 55/75\n","40/40 [==============================] - 21s 538ms/step - loss: 0.0820 - accuracy: 0.9752 - val_loss: 1.4136 - val_accuracy: 0.6765\n","Epoch 56/75\n","40/40 [==============================] - 22s 542ms/step - loss: 0.0810 - accuracy: 0.9751 - val_loss: 1.4040 - val_accuracy: 0.6751\n","Epoch 57/75\n","40/40 [==============================] - 22s 540ms/step - loss: 0.0990 - accuracy: 0.9685 - val_loss: 1.4571 - val_accuracy: 0.6618\n","Epoch 58/75\n","40/40 [==============================] - 22s 540ms/step - loss: 0.0936 - accuracy: 0.9703 - val_loss: 1.4326 - val_accuracy: 0.6706\n","Epoch 59/75\n","40/40 [==============================] - 22s 540ms/step - loss: 0.0884 - accuracy: 0.9717 - val_loss: 1.4221 - val_accuracy: 0.6697\n","Epoch 60/75\n","40/40 [==============================] - 22s 539ms/step - loss: 0.0843 - accuracy: 0.9731 - val_loss: 1.4305 - val_accuracy: 0.6701\n","Epoch 61/75\n","40/40 [==============================] - 21s 539ms/step - loss: 0.0815 - accuracy: 0.9740 - val_loss: 1.4540 - val_accuracy: 0.6705\n","Epoch 62/75\n","40/40 [==============================] - 22s 540ms/step - loss: 0.0803 - accuracy: 0.9744 - val_loss: 1.4304 - val_accuracy: 0.6746\n","Epoch 63/75\n","40/40 [==============================] - 22s 541ms/step - loss: 0.1045 - accuracy: 0.9659 - val_loss: 1.5596 - val_accuracy: 0.6325\n","Epoch 64/75\n","40/40 [==============================] - 22s 541ms/step - loss: 0.2388 - accuracy: 0.9181 - val_loss: 1.5609 - val_accuracy: 0.6124\n","Epoch 65/75\n","40/40 [==============================] - 22s 541ms/step - loss: 0.3161 - accuracy: 0.8926 - val_loss: 1.6199 - val_accuracy: 0.5928\n","Epoch 66/75\n","40/40 [==============================] - 22s 539ms/step - loss: 0.3001 - accuracy: 0.8976 - val_loss: 1.4718 - val_accuracy: 0.6316\n","Epoch 67/75\n","40/40 [==============================] - 22s 539ms/step - loss: 0.2298 - accuracy: 0.9233 - val_loss: 1.4776 - val_accuracy: 0.6400\n","Epoch 68/75\n","40/40 [==============================] - 22s 539ms/step - loss: 0.1764 - accuracy: 0.9419 - val_loss: 1.3913 - val_accuracy: 0.6630\n","Epoch 69/75\n","40/40 [==============================] - 21s 538ms/step - loss: 0.1237 - accuracy: 0.9599 - val_loss: 1.4280 - val_accuracy: 0.6687\n","Epoch 70/75\n","40/40 [==============================] - 21s 538ms/step - loss: 0.0962 - accuracy: 0.9689 - val_loss: 1.4189 - val_accuracy: 0.6738\n","Epoch 71/75\n","40/40 [==============================] - 22s 539ms/step - loss: 0.0798 - accuracy: 0.9751 - val_loss: 1.4202 - val_accuracy: 0.6800\n","Epoch 72/75\n","40/40 [==============================] - 22s 540ms/step - loss: 0.0713 - accuracy: 0.9780 - val_loss: 1.4364 - val_accuracy: 0.6807\n","Epoch 73/75\n","40/40 [==============================] - 21s 538ms/step - loss: 0.0618 - accuracy: 0.9807 - val_loss: 1.4482 - val_accuracy: 0.6831\n","Epoch 74/75\n","40/40 [==============================] - 22s 541ms/step - loss: 0.0550 - accuracy: 0.9835 - val_loss: 1.4644 - val_accuracy: 0.6814\n","Epoch 75/75\n","40/40 [==============================] - 22s 539ms/step - loss: 0.0501 - accuracy: 0.9849 - val_loss: 1.4805 - val_accuracy: 0.6812\n","[INFO] The CRNN model was successfully trained.\n"],"name":"stdout"},{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 Axes>"],"image/png":"iVBORw0KGgoAAAANSUhEUgAAAYIAAAEKCAYAAAAfGVI8AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/Il7ecAAAACXBIWXMAAAsTAAALEwEAmpwYAAA5mklEQVR4nO3dd3hUVfrA8e/JpBdIQhJa6L2ECERQkaIoIotiAcEuKqirrmXX7lrWXcuquxZUFhVd1y7qT8UOgqggJfTekpAQSEJCep85vz/OJISQkEkyk5lk3s/zzDNzy9x5M0nue++pSmuNEEII7+Xj7gCEEEK4lyQCIYTwcpIIhBDCy0kiEEIILyeJQAghvJwkAiGE8HIuSwRKqYVKqUyl1NZ6tiul1EtKqb1Kqc1KqRGuikUIIUT9XHlH8DYw+STbzwf62R9zgddcGIsQQoh6uCwRaK1XADkn2WUa8I42fgfClVKdXRWPEEKIuvm68bO7Aqk1ltPs6w7V3lEpNRdz10BISMjIgQMHtkiAQgjRViQmJh7RWkfXtc2diUDVsa7O8S601guABQAJCQl63bp1roxLCCHaHKVUSn3b3NlqKA3oVmM5Fkh3UyxCCOHxXDU2nDvvCL4EblNKfQiMBvK01icUCwkhRFtns2kO5paQkl1MSk4RB7KLST1aTE5ROXklleSXVJBXUsH1Y3py96QBTv98lyUCpdQHwAQgSimVBjwK+AForecD3wBTgL1AMTDbVbEIIYQnKK+0kZxdxN7MQvZkFLI3q5C9mYXszyqkrNJWvZ+/xYfYiCCiQgPoGh7E4M7taB/kx/AeES6Jy2WJQGt9eQPbNXCrqz5fCOEcNpsms6CMg7klpOeWkJFfytHicnKKKjhaVE5eSQVllVbKKm2UVdoor7Tho8BHKbA/+1l88LeY5wA/H4Z3i2DioBjiY8Px8amrutAor7SRU1ROdlEZ2YXHnvNLKymvtFFWabU/2ygpt1JcXklJhZUOoQE8eXEc7YP8WvCbOlFphZVV+7NZuiODVfuySc4uxmozxTtKQdfwIPrGhDKmTwf6xITSo0MwPTqE0KldIJaTfC/O5s6iISGEi5RWWMkqKCOzoIzswjL7ybScnKJySiusVFo1FVYbFTaNRUGArwV/Xx8CfH0otr+36pFZUEqF9fiyaV8fRUSIP5HB/rQL8iUkwJfIEB8CfC34WRQasGmwaY3Npqm02T/PaqOgtJJXl+9l3rK9RIUGcNaAaMIC/cgtKSev2BSB5BSVc6SwjPzSynp/Rn+Libcq7iB/C8H+vgT5W/hh22Hyiit4a/ap+FlatirUatMs2ZHB5+sPsmJPFsXlVoL8LJzepwOTh3aiX0wYfWNC6RMdSpC/pUVjq48kAiFaIatNk3a0mP1ZRSRnF5my5ewiDuQUk1lQRkE9J9BgfwtBfhb8LD742q/QrTZdfXVdVmkjwNeHmLBAYtoF0DsqhJh2gXSNCCI2PIgu4UF0ah9Iu0BflGr6FWtucTnLd2WxZEcG3287jNbQLsiP9vbHoC7tiArxp0NoAFGhAUSG+BMV6k+kfV1YgO9J7yQWJabxl0828cgXW3ny4rhmxeqowrJKPlmXytsrk0nJLqZjuwAuHt6VcwZ35PTeHQj084yTfl0kEQjhASqsNg7llnIwt4TD+SUczisjI7+U/JIKlFLVRS3FFdY6y5RD/C306BBCv5gwxvaLJjosoPoRFRJAZKg/HUL8PeZkFB7sz0XDu3LR8K4uOf70kbEkHyli3rK99I4KZc643i75HDAted74JYmXftpDQWklI7qHc9/kgUwa3BHfFr4baSpJBEK0sIz8UtYk5bA2OYfdGQWk5pRwKK8EW62WgWEBvrQP9kNXFbFojb+vD32iTZlyv46meKFnVAgdQvxb5Kq3Nbn73P4kZRfx5Lc76N4hmPOGdHL6Z5SUW7ln0SYWbz7E2QNjuO3svozo7poKXVeSRCCECxSXV7IxNZeDR0s4UlheXda+5WAeKdnFgCmmGdgpjISeEXSP7Eq3iGC6RgTRsV0gndoHEhog/57N4eOjeH5GPAePlnDnhxv58e5xxEYEO+34qTnF3PS/RHYczue+yQO5eXzvVpuM5S9NiGYqKbdyMLeY5CPFrE3JYfX+HLYezKOyxiV+sL+FqNAABnQK4+rTenBqz0iGdGnXaooOWqtAPwvzrhjOuH8u44M1B7jnPOcMT7N6fza3vLeeCquNhdeeylkDY5xyXHeRRCBEI+3LKuTrzYdYtiuTA9nFZBeVV2/zsyjiY8OZO643p/aKpHdUCFGhAYTI1b3bxEYEc9aAGD5el8ad5/RvdiuiHYfyuf7ttXRsH8gb1yTQOzrUSZG6j/x1CuGA5CNFfL3lEIs3H2LHoXyUguHdwpk0pCOxEcHERgQRGxHM4M7tPKZJoDjmitHdWfrfdSzZnsH5cU0f5DizoJQb/7uOsEA/PphzGh3bBToxSveRRCBEPVJziu0n/3S2HswHYGSPCB6ZOpgpcZ3p1L5tnAS8wYQBMXRpH8j7aw40ORGUVliZ+04iOUXlfHLz6W0mCYAkAiGqlZRbWZ2Uza97jvDr3iPsPFwAQHy3cB6aMogpwzrTNTzIzVGKprD4KGaN6s6/ftxNSnYRPTqENOr9Wmv+8skmNqbmMv+qkQzt2t5FkbqHJALh1VJzilm6I4OlOzNZvT+HcqsNf4sPCT0jeOD8gUyJ60y3SOe1NBHuM/PUbry4dA/vrznAA+cPatR7X1y6h8WbD3Hf5IFMHur8ZqjuJolAeJ28kgre/T2FLzemsyvDXPX3jg7hmtN7MLZ/NKN6Rko5fxvUsV0g5wyKYdG6NP587gD8fR2rNF6+K5MXluzhkhFduXm86zqmuZMkAuE1MvNLefO3JN77/QCFZZWM6hXJw38YxMRBHekV1biiAtE6XTG6B99vM8NaXBDfpcH9D+eVcvfHmxjYKazFhqpwB0kEos1LyS7iPyv2sygxjUqrjT8M68LN43szpEvbKucVDRvbN4pukUG8v/pAg4mg0mrjTx9soLTCyrwrRnjM8ByuIIlAtFnb0vOY//N+vt6cjq+PD5eOjOWmcb3pKVf/XsvHRzHr1O48+/0uftmTxdh+dU7hC8C/l+xmTXIOL8w8hb4xrb+vwMlIIhBtTmpOMY9/tY0lOzIJDfBlzrje3DCmFzFtqLmfaLorRnXn08Q0rlm4htvP6sufJvY7oYf3z7uzeGXZPmad2s1lA+N5EkkEos2otNp4e2Uyz/+wGx8Ff5nUn6tP7+n2yUmEZ4kI8eer28/k0S+38dJPe1m5L5sXZp1CsL8vS3dk8MP2DFbszmJgpzAeu3CIu8NtEcpVkyG7SkJCgl63bp27wxAeZlt6Hvd/uoUtB/M4Z1AMf5s2lC7S5l804IuNB3no861YbZqySis2DZ3bB3Lu4I7cNL5Pm+o3opRK1Fon1LVN7ghEq6a1ZuFvyTz1zQ7Cg/155YoRTInr1GZbdwjnmnZKV4Z3i+DFpXvoGh7IpCGdGNKlndf9/UgiEK1WQWkF9326mW+2HObcwR15dvowwoP93R2WaGW6dwjm+cvi3R2GW0kiEK3SzsP53PLueg7kFPPglIHMGdt6x4IXwt0kEQiPVmG18fn6gyxan8bRonIKyyopLK2koKySmLAAPphzGqN6Rbo7TCFaNUkEwiOVVVr5ZF0ary3fx8HcEvrbp2UMC/QlNNCXyGB/Zo7qRkyYNAkVorkkEQiPorXmq82HeOqbHRzKK+WUbuH8/aKhTBgQLUU/QriIJALhMTLyS3no860s2ZHBsNj2/HP6MM7sGyUJQAgXk0Qg3E5rzSeJaTyxeDvllTYenDKQ68f0kvl8hWghkghEiyurtLIu+SgbDhxlw4FcNqTmklNUzqiekTwzfZiMBCpEC5NEIFrUxtRc7v5oI/uPFAHQJzqEiQNjOLNfFBcM64KPjxQDCdHSJBGIFlFhtTHvp73MW7aXjmEBvHblCM7oE0X7YBkHSAh3k0QgXG5/ViF3fbSRTWl5XDK8K49eOEQGghPCg0giEC6VdKSIGfNXYdWaV68cwZS4zu4OSQhRiyQC4TIZ+aVc/eZqNPDpLWfQJ7ptT+4hRGsl7fOES+QVV3DNm2s4WlTO27NPlSQghAeTOwLhdCXlVm7471qSjhTx1uxTGRYb7u6QhBAnIXcEwqm2p+dz7VtrSDxwlBdmncKYvlHuDkkI0QCXJgKl1GSl1C6l1F6l1P11bG+vlPpKKbVJKbVNKTXblfEI19mfVcht769nyku/sPNQPs9Nj5eKYSFaCZcVDSmlLMArwLlAGrBWKfWl1np7jd1uBbZrrS9QSkUDu5RS72mty10Vl3Cu3OJynvluFx+vSyXA14fbzurLnHG9pXmoEK2IK+sIRgF7tdb7AZRSHwLTgJqJQANhyowqFgrkAJUujEk4idaaLzel87evtpNbUsHVp/Xg1rP6Eh0W4O7QhBCN5MpE0BVIrbGcBoyutc884EsgHQgDZmqtbbUPpJSaC8wF6N69u0uCFY5LO1rMw/+3leW7soiPbc+7N45mUOd27g5LCNFErkwEdQ0ao2stnwdsBM4G+gA/KqV+0VrnH/cmrRcACwASEhJqH0O0oD0ZBVzy6kqsWvPI1MFce0ZPLDI+kBCtmisTQRrQrcZyLObKv6bZwNNaaw3sVUolAQOBNS6MSzRRfmkFN/0vkQA/C5/dcgbdOwS7OyQhhBO4stXQWqCfUqqXUsofmIUpBqrpADARQCnVERgA7HdhTKKJtNb85eNNpOQU88oVwyUJCNGGuOyOQGtdqZS6DfgesAALtdbblFI327fPB54A3lZKbcEUJd2ntT7iqphE07328z5+2J7BX6cOZnTvDu4ORwjhRC7tWay1/gb4pta6+TVepwOTXBmDaL5f9xzhue93cUF8F64f09Pd4QghnEx6FouT2p6ez+0frKdvTChPXxIn8wcL0QZJIhD1+mlnBjPmryTQz8J/rk4gJECGphKiLZL/bHECrTVvr0zmicXbGdKlPW9em0BMu0B3hyWEcBFJBOI4lVYbf1u8nXdWpXDekI78e+YpBPvLn4kQbZn8h4tqlVYbd328ia82pXPTuN7cN3mgTCYvhBeQRCAAkwTu+GgjX28+xP3nD+Tm8X3cHZIQooVIIhBUWG3c+eFGvt5yiAenDGTuOEkCQngTSQRersJq408fbODbrYd5+A+DuHFsb3eHJIRoYZIIvFhphZXb3l/Pkh2ZkgSE8GKSCLxUUVklc95Zx8p92TwxbQhXn97T3SEJIdxEEoEXyi0u57q31rLlYB7/uiyeS0bEujskIYQbSSLwMlkFZVz95mr2ZxXxyhUjmDy0k7tDEkK4mSQCL5JfWsE1C9eQkl3Mm9clMLZftLtDEkJ4ABlryEuUVVq5+X+J7MkoYP7VIyUJCCGqyR2BF7DZNH/5ZDMr92Xz/Ix4xveXJCCEOEbuCLzAP77ZwVeb0rl38gAuHSkVw0KI40kiaOPe/DWJN39N4rozenKLDBshhKiDJII27Nc9R/jH19s5b0hH/jp1sEwqI4Sok9QRtFGpOcXc9sF6+kSH8vxlp2CRUUSF8Cw2KxQchtwDkJsCBYcgoB2ExkBIDIREgW8g+FhAWUD5gF8Q+Ac7PRRJBG1QSbmVuf9LxGrTLLgmgVCZWaxtsFZA0REI6wTOvruzVkBpHpTkQmUpaKs5UWkN1jIoL4byQqgoNvv6BoDF3zz7BpgTlm/gsddV2yz+ZtkvyPkxtwY2KxxYBdu/gAO/Q3kRVJRARZF5bats3PHG3AnnPu70MOUM0cZorbn3083sPJzPwutOpVdUiLtDEnXRGg6uh9KjENbFnNyDIk48WdqskPwrbP0UdnwFJTnmarHbKPPoNAwsfvaTts2cWErz7I9cKM03x/TxNQ9lgeJsKEiH/EPmKrQ425zgXcnib36+oAgIiYbT/ggDpzj3M0rzIX09HEyEilKY8AD4uKH0u6wAkn+D3d/BzsVQlGWSYffTIaqfSYp+IeAfAu1jIaIHhPcwfwNlhVCYYd5TlAXW8mO/W22DzvEuCVkSQRvz6vJ9fLUpnXvOG8BZA2LcHY7nKyuAzJ2Quc1c6fYYAzGDjj8hF2bB/uXmJFP1j2mrNI+So+ZRnANl+eZEF9YJwjqb58jeED0QovpDUDjk7IfNH8Pmj8zrmnwDISgS/ALBN8g856ZCUaY5cQycAl1GwKFNkLranGQa4uNnfhZbpTmRAPiHmvjadTY/b0gUBLaHwHDz7BdkiiGqiiQsfuak5RdsiiV8/Mz3YC2HyjLzsNqfK0vNSdhaZt9eDpUl5k6j5KhJZBnb4cPLYcS1MPkpc+wqaevg91ehMNPE6W8/YSofsFWY35G1wv57qLTHUWH2P7Ib0MeO1WW485NNXWw2OLQR9i6BfcsgbY2JzS8E+k+CwdOg77kQENrwsQLCzO+lhUkiaCO01vzrx928/NNeLojvwh8neGkLoYpSc4Lc9rk5CZ9+G4R1PH6fnP3w20uw7ydTNltbSDT0HAvtukDSCji82az3DTInSR/LsSvswHAIjoCOQ8w/cWmuKfdN/tVcbde89Q/uYK6+UdBrLIz9i4mxwH5lnp9u3l9Raj+hlkDPHuZE0m/SiWXDhZmQtdO8Vj7mpO1jMeXMQeHHTupVbPY7Bl//Zn3FzVZZDsv+bn4Hyb/CJa+bJPHbC5D8i/lOYwZBfpopPikrBLRJQBY/c3dh8TPfv8XPrI/sDXHToetIc5e0YAKsmue6RFBRYk76u7+F3T9A4WFAmSv2M26H3mdBt9EmmbcCSmvd8F4eJCEhQa9bt87dYXgUq03z8P9t5YM1B7gsIZYnL47D1+JFDcJsNnOy3vSBudIuOQqhncyVtMXfXHmOucNc/f/yPGxdZE4e/c8zJ42OgyFmsDmZJv9iTv5JK8ytebfR0Ods8+gcb060DsdlNYkmazcc2QVH9kCHPhA3wxQJeLukX+Dzm80JH0wR2Rm3md+XI1fPJ7PyZfjhYZizDLqOaH6sNZUVwMLJkLHVJN0+Z8OA86HvOebuykMppRK11gl1bpNE0LqVVli588ONfLftMH+c0Id7zhvQNpuJ2mymsrI015R/F2VBWiKk/g6pa6Esz5z0B06FEddAr/FwNAl+/Rds+hBQpmjBLwQSZpurtrCTDLintb1S1M1Xz21dSa4pCgrvDnGXOe/7Ls2Hfw+BfufC9IXOOSaYv8OProTd38MlC2DQha3mb0QSQRultWb222tZviuLR6YO5voze7k7JOcrzIQVz0Lif025c23Rg6D7aOh2mrnCD448cZ+jKbBmgSm6GTW37n1E2/P9Q/D7a3DHJgjv5pxjLv2buas8/58w+ibnHLOFnCwRSB1BK7YoMY3lu7J47ILBXDemFSWBFc/BuoWmvXRVpWq7rqZcOGYQhPeE8gJThvz7q6YSMn6W2VZVqRkUAZ2GmueGRPSA8/7h6p9KeJrRN5tEsHq+c37/WxaZJDDiWnNB0YZIImilcorKefKbHST0iOAad88udniLad9u8T9WkddxiHmuLfcA/PyMaUkTFGmWU9dA8ZFj+/gGmYrA8gIYcgmc/bApWxeiMcK7wZCLzd3k+HvNRURTHVwPX9wK3c+AKc+1uT4Rkghaqae+2UFBaSX/uDgOH3f0GtbaNKn85XlTwVpb9zPgusUnVq4uexJQcPkHx1eYlhVC1i7I3A6ZO0yZ/6i5Lms3LbzEGbeZxgHr3zH1Qo2Vvc/cVWx8z/TfmPm/VlMn0BiSCFqh1fuz+SQxjZvH92FApzDXfpjWkPKbueJXyrSsKSuAtW+YjjthneG8J0379qo23Yc2wk9PwK//hnF/OXasw1tMxe0Zt5/YaiYgFGJHmocQztJlOPQ4E36fD6NucvwkfjARfvkX7Pza3J3GzTB3FR7cKqg5JBG0MuWVNh76v63ERgRxx8R+rv/ADf+DL+u4kgrvAVP/DadcaYYSqKnvRMjYBsufMk3rqprvLXnM3J6PvdvlYQtRbexd8O6l8PlNcOkbDTcBzkuDheebfhtj/wyj5py8hVkbIImglVmwYh97Mwt567pTCfJvRJv2psg/BN8/bIp5/vCcuTvQNnNnED0ILPX8+SgFU/9lxlb5bC7ctALS1pqel+c+4VgFrxDO0vccOPdv8OMjppfyhS+fvIz/txfNWEtzfzYNDbyAJIJW5P3VB3hhyR6mxHXirIFOGD6i5ChseM8U2Uz6O4TWmLlMa/j6z6bJ5rR5ja+sDYqAi1+Dd6aZjj3p66FdbJtrbSFaiaoOhSueNZ3AzvtH3cmg4LCpXI6/3GuSAEgiaBUqrTb+/vUO3l6ZzPj+0Tx96bDmHfDQZlj7Omz+xIwDoyxmfJSrPoNIezPUbZ/Drq/NlVRTW+z0ngCn3Qq/v2KWL3qt1XS5F23QWQ+Zjma/vwKB7WDC/Sfus/JlMwyHlxVfujQRKKUmAy8CFuANrfXTdewzAXgB8AOOaK3HuzKm1iavpILbP9jAit1Z3HBmLx6cMqjxcwscTYGUlabSN2Ul5OwzTTSHzYBT55hxbd6/DN6cBFd9aipyv7nHVLSddmvzfoCJj0DyCpNshs1s3rGEaA6lYPLTpof68qfMIHpj/nRse9ER078lboYZu8iLuCwRKKUswCvAuUAasFYp9aXWenuNfcKBV4HJWusDSikZLrOG3OJyps9fRfKRIp6+JI5Zo7o37gDlRfDpHHNlD6YjVo8zTI/IYZcdX1Z//ffwv0vgrSnQ5RQzlMOFX9RfD+Aov0C48SdT5tqYcXqEcAUfH7jgJfO/8eNfzYivo+3FlavmmcHkxv7ZvTG6gSvvCEYBe7XW+wGUUh8C04DtNfa5AvhMa30AQGud6cJ4WhWtNX/5ZBMp2UW8c/0ozujbyGZrRUfMVX76BpjwIAyaaip46xufPXoA3PCDaV2R/AuMv8/03HWGNtjuWrRiFl/TeshaAd/eY1q9DboA1rxuOqBF93d3hC3OlYmgK5BaYzkNGF1rn/6An1JqORAGvKi1fqf2gZRSc4G5AN27N/KquJV645ckluzI5NELBjc+CeQkmRN6/kGY+S4M/INj72vfFa7/FnZ+Y26PhWirLH4w4y348Ar46g7Y8okpMhp3j7sjcwtXjlVcV0F27RHufIGRwB+A84C/KqVOSMda6wVa6wStdUJ0dHTtzW1OYspRnvluJ5OHdOK6M3o27s3pG0xZf0kOXPOl40mgSlAEDL9SruJF2+cbYC6Uep5p7oIHXWCGJPdCDd4RKKWmAt9oXTW9kcPSgJpD/sUC6XXsc0RrXQQUKaVWAPHA7kZ+VptxtKic299fT+fwQJ6ZPszxIaUry+GX58yQD2Gd4arFprhHCFE/vyC44iPTC3741e6Oxm0cuSOYBexRSv1TKTWoEcdeC/RTSvVSSvnbj/NlrX2+AMYqpXyVUsGYoqMdjfiMNsVm0/z5k00cKSzn1StG0j6ojkHb6pKWCP8ZZwZzG3KJ6QgjSUAIx/iHmIENvajfQG0N3hFora9SSrUDLgfeUkpp4C3gA611wUneV6mUug34HtN8dKHWeptS6mb79vla6x1Kqe+AzYAN08R0a/N/rNbpvTUH+GlnJo9dMJi4WAdGSrTZzJg+v71g7gKu+NiMyS+EEI3g8MQ0Sqko4CrgTsxVe1/gJa31yy6Lrg5tdWKa1JxiznthBSN7RPDO9aMaLhKyVsJXfzKjIg6/Cs57ynSSEUKIOjRrYhql1AXA9UAf4H/AKK11pr0oZwfQoomgLbLZNPd9uhkfpXj6UgfqBSrL4bMbYfsXMOEB09SzjY2PLoRoOY40H50B/FtrvaLmSq11sVLqeteE5V3eX3OAlfuyeeqSOLqGB51854oS+Pga2PMDTPqHGW9dCCGawZFE8ChwqGpBKRUEdNRaJ2utl7osMi+RmlPMU9/sYGy/KGad2sC8qtYKeH8mJK2AqS+YSdiFEKKZHGk19AmmIreK1b5ONJPWmvs/24xytEjo52cg6WczGqgkASGEkziSCHy11uVVC/bX0tvICd79PYXf9mbz4JRBDRcJJf9qJn0/5SpTOSyEEE7iSCLIUkpdWLWglJoGHDnJ/sIB+7IK+cc3OxjfP5rLRzVQJFRy1EzwEtkLzn+mZQIUQngNR+oIbgbeU0rNwwwbkQpc49Ko2rgKq427PtpIoJ+FZxvqPay1GQulMMMMChcQ2nKBCiG8giMdyvYBpymlQjH9DurtRCYc8/LSPWxOy+O1K0cQ066BiVo2vGuaiZ7zGHSVid2FEM7n0OijSqk/AEOAwKqrV63131wYV5uVmHKUecv2cumIWM6P63zynfMOwrf3Qq9xcMYdLROgEMLrNFhHoJSaD8wEbscUDc0AvHdQjmYoKqvk7o830rl9EI9d6MAoh8ufMtPmXfhy/fMICCFEMzlydjlDa30NcFRr/ThwOsePKiocNG/ZXg7kFPOvy+IJC2xgQLmsXWb4iIQbIKJni8QnhPBOjiSCUvtzsVKqC1AB9HJdSG1TVkEZb/+WzLT4Lozu3aHhNyz9G/iFwLi/uD44IYRXcyQRfGWfW/hZYD2QDHzgwpjapFeX76XcauOOcxyYBi91DexcbCbWDmnk7GRCCNFIJ60sVkr5AEu11rnAp0qpxUCg1jqvJYJrK9JzS3jv9wNMHxFLr6iQk++sNfz4KITEwGl/bJkAhRBe7aR3BPZZyZ6vsVwmSaDx5i3bi0Zz+8S+De+85wc4sBLG3yt9BoQQLcKRoqEflFKXKofnTBQ1Hcgu5uO1qVw+qjuxEcEn39lmhSWPQ2RvGHldi8QnhBCO9CO4GwgBKpVSpZgmpFprLbOgOODFpXuw+ChuPcuBu4GUlZC5DS5eABYHp6kUQohmcqRncVhLBNIW7c0s4PMNadxwZi86NtSDGEwiQMl0k0KIFuXIDGXj6lpfe6IacTytNY9/tZ0gPws3j+/j2JtSfoNOQyEo3KWxCSFETY4UDd1T43UgMApIBM52SURtxHurD/DLniM8cdFQOoQGNPwGawWkrYURMp6fEKJlOVI0dEHNZaVUN+CfLouoDTiQXcyT3+zgzL5RXDW6u2NvOrQJKoqhxxmuDU4IIWppygA2acBQZwfSVthsmnsWbcKiFM80NMR0TSm/mefup7suOCGEqIMjdQQvA9q+6AOcAmxyYUyt2tsrk1mdlMM/pw9reNaxmlJWQod+EBrjuuCEEKIOjtQRrKvxuhL4QGv9m4viadX2ZRXyzHc7mTgwhhkjYx1/o80KKatgyEUui00IIerjSCJYBJRqra0ASimLUipYa13s2tBan2e+3UmArw9PXRLneJEQQOZ2KMuDHmNcF5wQQtTDkTqCpUDNMo4gYIlrwmm98oorWLYrkxkJ3Rqeday2lFXmuYfUDwghWp4jiSBQa11YtWB/3cBYCd7nu22HqLBqLozv0vg3p/wG7btBuIMtjIQQwokcSQRFSqkRVQtKqZFAietCap2+2JhOzw7BDItt37g3am0qiqXZqBDCTRypI7gT+EQplW5f7oyZulLYZeaXsmp/Nref1bdxdQMA2fugKFMSgRDCbRzpULZWKTUQGIAZcG6n1rrC5ZG1Ios3H0JruPCUJhYLAXSXRCCEcA9HJq+/FQjRWm/VWm8BQpVSMmNKDV9uSmdw53b0jWnC+HwHVkFwFET1c35gQgjhAEfqCObYZygDQGt9FJjjsohamZTsIjam5jbtbgDMHUGPM0CmexBCuIkjicCn5qQ0SikL4O+6kFqXrzaZqpMLmtJaKDcVcg9I/wEhhFs5Uln8PfCxUmo+ZqiJm4FvXRpVK/LlpnRO7RnRuOEkqqSsNM/Sf0AI4UaO3BHch+lUdgtwK7CZ4zuYea2dh/PZnVHYtL4DACm/QkB76Chj+Akh3KfBRGCfwP53YD+QAEwEdjhycKXUZKXULqXUXqXU/SfZ71SllFUpNd3BuD3CFxvTsfgopsR1btoBku31Az4W5wYmhBCNUG/RkFKqPzALuBzIBj4C0Fqf5ciB7XUJrwDnYoauXquU+lJrvb2O/Z7BFEG1GqUVVhYlpjGuX5RjE8/Uln8IcvZBwmznByeEEI1wsjuCnZir/wu01mdqrV8GrI049ihgr9Z6v9a6HPgQmFbHfrcDnwKZjTi22y1KTCOroIw543o37QBV/Qd6num8oIQQoglOlgguBQ4Dy5RSryulJmI6lDmqK5BaYznNvq6aUqorcDEw/2QHUkrNVUqtU0qty8rKakQIrlFptfGfFfsY3j2c03t3aNpBkn+FgHbQaZhzgxNCiEaqNxForT/XWs8EBgLLgbuAjkqp15RSkxw4dl1JQ9dafgG4r2qI65PEskBrnaC1ToiOjnbgo11r8eZDpOaU8McJTRhSokryr9D9NKkfEEK4nSOVxUVa6/e01lOBWGAjUG/Fbw1pQLcay7FAeq19EoAPlVLJwHTgVaXURQ4c221sNs2ry/cyoGMYEwc2cTaxggzI3iPFQkIIj9CoOYu11jla6/9orc92YPe1QD+lVC+llD+m4vnLWsfrpbXuqbXuiZkA549a6/9rTEwtbenOTHZnFHLLhD74+DTxbiDlV/PcQxKBEML9HOlQ1iRa60ql1G2Y1kAWYKHWeptS6mb79pPWC3girTWvLNtLt8ggpg5rYpNRMM1G/UOhc7zzghNCiCZyWSIA0Fp/A3xTa12dCUBrfZ0rY3GGVfuz2Ziay98vGoqvpVE3U8erqh+wuPTrF0IIhzTjbOZdtNa8vHQv0WEBTG/MxPS1FWbBkV0yvpAQwmNIInDQosQ0M/nM2X0J9GtGS5/q/gNjnROYEEI0kyQCBxzKK+Fvi7czqlckV43u0byDJf8KfiHQ5RSnxCaEEM0liaABWmvu/3QLlVbNs9OHNb2lUJWU36DbKLD4OSdAIYRoJkkEDfhkXRo/787i/vMH0qNDSPMOVpQNmdul/4AQwqNIIjiJ9NwSnli8ndN6R3L1ac0sEgIZX0gI4ZEkEdQjq6CMP3+8CavW/PPS+OYXCQEk/wK+QdBlRPOPJYQQTiIN2WvJL63g9RX7efPXJMoqbTx1SRzdOwQ75+BJK8xsZL4y06cQwnNIIrArrbDyzqpkXl2+j9ziCqYO68yfJw2gV1Qz6wWqFGZC1k6In+Wc4wkhhJN4fSIoq7TyweoDvLJ8H1kFZYzrH8295w1gaNf2zv2gpBXmudc45x5XCCGayWsTgdaaD9em8tLSPRzKK2VUr0hevnw4pzV1foGGJK0w8xN3kvGFhBCexWsTwVu/JfO3xdsZ0T2c52bEc0afDk2fW8ARSSug5xgZX0gI4XG88qy0N7OAZ77bycSBMbxxbYJrEwBAbiocTYLRN7n2c4QQogm8rvlohdXGXR9tItjfwlOXxrk+CYBpNgoyvpAQwiN53R3Byz/tZcvBPOZfNYKYsMCW+dCkFRDcAWIGt8znCSFEI3jVHcHG1FxeWbaXS0Z0ZfLQZkws0xha2+sHxoKPV33dQohWwmvOTCXlVu7+aCMdwwJ47MIhLffBOfsh/6A0GxVCeCyvKRr6alM6+48U8f6No2kX2IIjfyb9bJ57jW+5zxRCiEbwmkQwIyGWwV3aOb+jWEOSVkBYF+jQp2U/VwghHOQ1RUNKqZZPAlpD0i/Qayy0ROskIYRoAq9JBG6RuQOKj0j9gBDCo0kiaI4Vz8JvL9a/XcYXEkK0ApIImmPdW7Dmjfq3p66GdrEQ3r3lYhJCiEbymspipyvOMc1CAYqOQEjUifscTITYkS0blxBCNJLcETRV5vZjrw+uP3F7UTbkpkBXSQRCCM8miaCpMmomgsQTt6fbk4NMSymE8HBSNNRUGVshKBJCY46d9Gs6mAgo6HJKS0cmhBCNIncETZWxDToOMUU/B9ebPgM1HUyE6IEQEOae+IQQwkGSCJrCZjN1BB2HQpfhpq9AXuqx7Vqb5NBVioWEEJ5PEkFTHE2CimLoOPhYZXDNeoLcAyY5SCIQQrQCkgiaoqrFUMch5q7A4n98y6GqpCAthoQQrYAkgqbI2AYoiB4Evv4mGaRvOLY9fb1JDjEtONy1EEI0kSSCpsjYakYT9Q82y11HmkRgs5rlg+uh0zCTJIQQwsNJImiKqhZDVbqOgPJCOLLHJIP0jVIsJIRoNVyaCJRSk5VSu5RSe5VS99ex/Uql1Gb7Y6VSKt6V8ThFeRHkJB1f7FPVaSx9PWTtgooiqSgWQrQaLutQppSyAK8A5wJpwFql1Jda6xpdckkCxmutjyqlzgcWAKNdFZNTZO4E9PF3BFH9wD/MFAlVFQ/JHYEQopVwZc/iUcBerfV+AKXUh8A0oDoRaK1X1tj/dyDWhfE4R8ZW81wzEfhYTA/ig4lgq4SAdhApM5IJIVoHVxYNdQVq9LIizb6uPjcA39a1QSk1Vym1Tim1Lisry4khNkHGNvAPhfAex6/vMtwkidTV5rWPVL8IIVoHV56t6pqbUdexDqXUWZhEcF9d27XWC7TWCVrrhOjoaCeG2AQZ2yBm0Ikn+q4jwVpu+hhIsZAQohVxZSJIA7rVWI4F0mvvpJQaBrwBTNNaZ7swnubT2lz1d6yjf0DNymGpKBZCtCKuTARrgX5KqV5KKX9gFvBlzR2UUt2Bz4Crtda7XRiLcxQcgtJc04GstvbdINg+OY3cEQghWhGXVRZrrSuVUrcB3wMWYKHWeptS6mb79vnAI0AH4FWlFECl1jrBVTE1W8Y281zXHYFSEHsqHN4M7bq0bFxCCNEMLp2PQGv9DfBNrXXza7y+EbjRlTE4VVWLoZjBdW+f8k8ozWu5eITwABUVFaSlpVFaWuruUAQQGBhIbGwsfn5+Dr9HJqZpjIxtZjL6oPC6t8sk9cILpaWlERYWRs+ePbHf2Qs30VqTnZ1NWloavXr1cvh90sbRUQWH4cDvdRcLCeHFSktL6dChgyQBD6CUokOHDo2+O5NE4IikFTB/LBRnw+i57o5GCI8jScBzNOV3IYngZGw2+PlZeGeaKQ6a8xP0PcfdUQkhhFN5Tx1B8m/wy/ONe09RJhzeAnEzYOoLEBDqktCEEMKdvCcR2Coa36LHN8gkgJHXmeahQgivVllZia9v2ztttr2fqD69J5iHEMJlHv9qG9vT8516zMFd2vHoBQ030rjoootITU2ltLSUO+64g7lz5/Ldd9/x4IMPYrVaiYqKYunSpRQWFnL77bezbt06lFI8+uijXHrppYSGhlJYWAjAokWLWLx4MW+//TbXXXcdkZGRbNiwgREjRjBz5kzuvPNOSkpKCAoK4q233mLAgAFYrVbuu+8+vv/+e5RSzJkzh8GDBzNv3jw+//xzAH788Udee+01PvvsM6d+R83lPYlACNGmLVy4kMjISEpKSjj11FOZNm0ac+bMYcWKFfTq1YucnBwAnnjiCdq3b8+WLVsAOHr0aIPH3r17N0uWLMFisZCfn8+KFSvw9fVlyZIlPPjgg3z66acsWLCApKQkNmzYgK+vLzk5OURERHDrrbeSlZVFdHQ0b731FrNnz3bp99AUkgiEEE7jyJW7q7z00kvVV96pqaksWLCAcePGVbenj4yMBGDJkiV8+OGH1e+LiIho8NgzZszAYrEAkJeXx7XXXsuePXtQSlFRUVF93Jtvvrm66Kjq866++mreffddZs+ezapVq3jnnXec9BM7jyQCIUSrt3z5cpYsWcKqVasIDg5mwoQJxMfHs2vXrhP21VrX2cSy5rra7fBDQkKqX//1r3/lrLPO4vPPPyc5OZkJEyac9LizZ8/mggsuIDAwkBkzZnhkHYM0HxVCtHp5eXlEREQQHBzMzp07+f333ykrK+Pnn38mKSkJoLpoaNKkScybN6/6vVVFQx07dmTHjh3YbLbqO4v6PqtrVzO1yttvv129ftKkScyfP5/KysrjPq9Lly506dKFv//971x33XVO+5mdSRKBEKLVmzx5MpWVlQwbNoy//vWvnHbaaURHR7NgwQIuueQS4uPjmTlzJgAPP/wwR48eZejQocTHx7Ns2TIAnn76aaZOncrZZ59N586d6/2se++9lwceeIAxY8ZgtVqr19944410796dYcOGER8fz/vvv1+97corr6Rbt24MHlzPOGVuprSuc64Yj5WQkKDXrVvn7jCEEHY7duxg0KBB7g7Do912220MHz6cG264oUU+r67fiVIqsb7RnT2vsEoIIdqQkSNHEhISwvPPN7JDawuSRCCEEC6UmJjo7hAaJHUEQgjh5SQRCCGEl5NEIIQQXk4SgRBCeDlJBEII4eUkEQghvEpoqMwrUps0HxVCOM+395vJnJypUxyc/7Rzj+kBPGluA7kjEEK0avfddx+vvvpq9fJjjz3G448/zsSJExkxYgRxcXF88cUXDh2rsLCw3ve988471cNHXH311QBkZGRw8cUXEx8fT3x8PCtXriQ5OZmhQ4dWv++5557jscceA2DChAk8+OCDjB8/nhdffJGvvvqK0aNHM3z4cM455xwyMjKq45g9ezZxcXEMGzaMTz/9lDfffJO77rqr+rivv/46d999d5O/t+NorVvVY+TIkVoI4Tm2b9/u1s9fv369HjduXPXyoEGDdEpKis7Ly9Naa52VlaX79OmjbTab1lrrkJCQeo9VUVFR5/u2bt2q+/fvr7OysrTWWmdnZ2uttb7sssv0v//9b6211pWVlTo3N1cnJSXpIUOGVB/z2Wef1Y8++qjWWuvx48frW265pXpbTk5OdVyvv/66vvvuu7XWWt977736jjvuOG6/wsJC3bt3b11eXq611vr000/XmzdvrvPnqOt3AqzT9ZxXPeO+RAghmmj48OFkZmaSnp5OVlYWERERdO7cmbvuuosVK1bg4+PDwYMHycjIoFOnTic9ltaaBx988IT3/fTTT0yfPp2oqCjg2FwDP/30U/X8AhaLhfbt2zc40U3V4HcAaWlpzJw5k0OHDlFeXl49d0J9cyacffbZLF68mEGDBlFRUUFcXFwjv626SSIQQrR606dPZ9GiRRw+fJhZs2bx3nvvkZWVRWJiIn5+fvTs2fOEOQbqUt/7dD1zDdTF19cXm81WvXyyuQ1uv/127r77bi688EKWL19eXYRU3+fdeOONPPnkkwwcONCpM51JHYEQotWbNWsWH374IYsWLWL69Onk5eURExODn58fy5YtIyUlxaHj1Pe+iRMn8vHHH5OdnQ0cm2tg4sSJvPbaawBYrVby8/Pp2LEjmZmZZGdnU1ZWxuLFi0/6eVVzG/z3v/+tXl/fnAmjR48mNTWV999/n8svv9zRr6dBkgiEEK3ekCFDKCgooGvXrnTu3Jkrr7ySdevWkZCQwHvvvcfAgQMdOk597xsyZAgPPfQQ48ePJz4+vrqS9sUXX2TZsmXExcUxcuRItm3bhp+fH4888gijR49m6tSpJ/3sxx57jBkzZjB27NjqYieof84EgMsuu4wxY8Y4NMWmo2Q+AiFEs8h8BC1r6tSp3HXXXUycOLHefRo7H4HcEQghRCuQm5tL//79CQoKOmkSaAqpLBZCeJ0tW7ZU9wWoEhAQwOrVq90UUcPCw8PZvXu3S44tiUAI0WyNaVXjCeLi4ti4caO7w3CJphT3S9GQEKJZAgMDyc7ObtIJSDiX1prs7GwCAwMb9T65IxBCNEtsbCxpaWlkZWW5OxSBScyxsbGNeo8kAiFEs/j5+VX3iBWtk0uLhpRSk5VSu5RSe5VS99exXSmlXrJv36yUGuHKeIQQQpzIZYlAKWUBXgHOBwYDlyulBtfa7Xygn/0xF3jNVfEIIYSomyvvCEYBe7XW+7XW5cCHwLRa+0wD3rEPjvc7EK6U6uzCmIQQQtTiyjqCrkBqjeU0YLQD+3QFDtXcSSk1F3PHAFColNrVxJiigCNNfG9Lag1xSozOITE6h8TYsB71bXBlIqirUXHt9mWO7IPWegGwoNkBKbWuvi7WnqQ1xCkxOofE6BwSY/O4smgoDehWYzkWSG/CPkIIIVzIlYlgLdBPKdVLKeUPzAK+rLXPl8A19tZDpwF5WutDtQ8khBDCdVxWNKS1rlRK3QZ8D1iAhVrrbUqpm+3b5wPfAFOAvUAx4LyZFurW7OKlFtIa4pQYnUNidA6JsRla3TDUQgghnEvGGhJCCC8niUAIIbyc1ySChoa7cAel1EKlVKZSamuNdZFKqR+VUnvsz86bj65pMXZTSi1TSu1QSm1TSt3haXEqpQKVUmuUUpvsMT7uaTHWiNWilNqglFrswTEmK6W2KKU2KqXWeWKcSqlwpdQipdRO+9/m6Z4Uo1JqgP37q3rkK6Xu9KQYa/KKRODgcBfu8DYwuda6+4GlWut+wFL7sjtVAn/WWg8CTgNutX93nhRnGXC21joeOAWYbG+F5kkxVrkD2FFj2RNjBDhLa31KjXbvnhbni8B3WuuBQDzmO/WYGLXWu+zf3ynASExjmM89KcbjaK3b/AM4Hfi+xvIDwAPujsseS09ga43lXUBn++vOwC53x1gr3i+Acz01TiAYWI/pxe5RMWL6ySwFzgYWe+rvG0gGomqt85g4gXZAEvbGLp4YY624JgG/eXKMXnFHQP1DWXiijtrel8L+HOPmeKoppXoCw4HVeFic9iKXjUAm8KPW2uNiBF4A7gVsNdZ5Woxgevf/oJRKtA/vAp4VZ28gC3jLXsz2hlIqxMNirGkW8IH9tUfG6C2JwKGhLET9lFKhwKfAnVrrfHfHU5vW2qrNbXgsMEopNdTNIR1HKTUVyNRaJ7o7FgeM0VqPwBSl3qqUGufugGrxBUYAr2mthwNFeEoRSy32zrQXAp+4O5aT8ZZE0JqGssioGoHV/pzp5nhQSvlhksB7WuvP7Ks9Lk4ArXUusBxT9+JJMY4BLlRKJWNG4j1bKfUunhUjAFrrdPtzJqZcexSeFWcakGa/6wNYhEkMnhRjlfOB9VrrDPuyJ8boNYnAkeEuPMWXwLX219diyuTdRimlgDeBHVrrf9XY5DFxKqWilVLh9tdBwDnATjwoRq31A1rrWK11T8zf309a66vwoBgBlFIhSqmwqteY8u2teFCcWuvDQKpSaoB91URgOx4UYw2Xc6xYCDwzRu+oLLZXzEwBdgP7gIfcHY89pg8wQ25XYK5ybgA6YCoU99ifI90c45mYYrTNwEb7Y4onxQkMAzbYY9wKPGJf7zEx1op3Ascqiz0qRkz5+yb7Y1vV/4oHxnkKsM7+O/8/IMIDYwwGsoH2NdZ5VIxVDxliQgghvJy3FA0JIYSohyQCIYTwcpIIhBDCy0kiEEIILyeJQAghvJwkAiFqUUpZa40c6bReq0qpnjVHmxXCE7hsqkohWrESbYarEMIryB2BEA6yj9P/jH3ugzVKqb729T2UUkuVUpvtz93t6zsqpT63z5OwSSl1hv1QFqXU6/a5E36w94YWwm0kEQhxoqBaRUMza2zL11qPAuZhRhPF/vodrfUw4D3gJfv6l4CftZknYQSmpy5AP+AVrfUQIBe41KU/jRANkJ7FQtSilCrUWofWsT4ZMwHOfvtAfIe11h2UUkcwY8xX2Ncf0lpHKaWygFitdVmNY/TEDJPdz758H+Cntf57C/xoQtRJ7giEaBxdz+v69qlLWY3XVqSuTriZJAIhGmdmjedV9tcrMSOKAlwJ/Gp/vRS4BaonzmnXUkEK0RhyJSLEiYLss51V+U5rXdWENEAptRpzEXW5fd2fgIVKqXswM2fNtq+/A1iglLoBc+V/C2a0WSE8itQRCOEgex1Bgtb6iLtjEcKZpGhICCG8nNwRCCGEl5M7AiGE8HKSCIQQwstJIhBCCC8niUAIIbycJAIhhPBy/w8WxO7gXbkmTwAAAABJRU5ErkJggg==\n"},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["17/17 - 2s - loss: 1.4805 - accuracy: 0.6812\n","\n"," Dev accuracy:  68.12 % \n","\n"],"name":"stdout"},{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 2 Axes>"],"image/png":"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\n"},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"qR68vnSUTb6j"},"source":["### *not shifted CQT - 65.35%* "]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"__xTWP39V3Pc","executionInfo":{"status":"ok","timestamp":1615026455781,"user_tz":-60,"elapsed":17225877,"user":{"displayName":"Vojtech Lanz","photoUrl":"","userId":"15791523486540207166"}},"outputId":"700adb6e-ad8f-4ca0-fe70-35140a96e950"},"source":["#!/usr/bin/env python3\n","# Prepare data\n","x, y = IsophonicsDataset.load_preprocessed_dataset(\"./drive/MyDrive/Isophonics1000.ds\")\n","n_samples, n_frames, n_chromas = x.shape\n","x = x.reshape((n_samples,n_frames,n_chromas,1))\n","train_x, dev_x, train_y, dev_y = sklearn.model_selection.train_test_split(x, y, test_size=0.3, random_state=args.seed)\n"," \n","# Create model\n","crnn_model = CRNN_1(input_shape=(n_frames, n_chromas, 1), output_classes=25)\n"," \n","# Train model\n","crnn_model.fit(train_x, train_y, dev_x, dev_y, epochs=36)\n"," \n","# Display score\n","crnn_model.display_training_progress()\n","print(\"\\n\",\"Dev accuracy: \", \"{:.2f}\".format(100*crnn_model.score(dev_x, dev_y)), \"%\", \"\\n\")\n","crnn_model.display_confusion_matrix(dev_x, dev_y)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["[INFO] The Preprocessed Isophonics Dataset was loaded successfully.\n","Model: \"sequential\"\n","_________________________________________________________________\n","Layer (type)                 Output Shape              Param #   \n","=================================================================\n","conv2d (Conv2D)              (None, 1000, 252, 16)     160       \n","_________________________________________________________________\n","batch_normalization (BatchNo (None, 1000, 252, 16)     64        \n","_________________________________________________________________\n","conv2d_1 (Conv2D)            (None, 1000, 252, 16)     2320      \n","_________________________________________________________________\n","batch_normalization_1 (Batch (None, 1000, 252, 16)     64        \n","_________________________________________________________________\n","conv2d_2 (Conv2D)            (None, 1000, 252, 16)     2320      \n","_________________________________________________________________\n","batch_normalization_2 (Batch (None, 1000, 252, 16)     64        \n","_________________________________________________________________\n","max_pooling2d (MaxPooling2D) (None, 1000, 84, 16)      0         \n","_________________________________________________________________\n","conv2d_3 (Conv2D)            (None, 1000, 84, 32)      4640      \n","_________________________________________________________________\n","batch_normalization_3 (Batch (None, 1000, 84, 32)      128       \n","_________________________________________________________________\n","conv2d_4 (Conv2D)            (None, 1000, 84, 32)      9248      \n","_________________________________________________________________\n","batch_normalization_4 (Batch (None, 1000, 84, 32)      128       \n","_________________________________________________________________\n","conv2d_5 (Conv2D)            (None, 1000, 84, 32)      9248      \n","_________________________________________________________________\n","batch_normalization_5 (Batch (None, 1000, 84, 32)      128       \n","_________________________________________________________________\n","max_pooling2d_1 (MaxPooling2 (None, 1000, 28, 32)      0         \n","_________________________________________________________________\n","conv2d_6 (Conv2D)            (None, 1000, 28, 64)      18496     \n","_________________________________________________________________\n","batch_normalization_6 (Batch (None, 1000, 28, 64)      256       \n","_________________________________________________________________\n","conv2d_7 (Conv2D)            (None, 1000, 28, 64)      36928     \n","_________________________________________________________________\n","batch_normalization_7 (Batch (None, 1000, 28, 64)      256       \n","_________________________________________________________________\n","max_pooling2d_2 (MaxPooling2 (None, 1000, 7, 64)       0         \n","_________________________________________________________________\n","conv2d_8 (Conv2D)            (None, 1000, 7, 80)       46160     \n","_________________________________________________________________\n","batch_normalization_8 (Batch (None, 1000, 7, 80)       320       \n","_________________________________________________________________\n","conv2d_9 (Conv2D)            (None, 1000, 7, 80)       57680     \n","_________________________________________________________________\n","batch_normalization_9 (Batch (None, 1000, 7, 80)       320       \n","_________________________________________________________________\n","reshape (Reshape)            (None, 1000, 560)         0         \n","_________________________________________________________________\n","bidirectional (Bidirectional (None, 1000, 192)         504576    \n","_________________________________________________________________\n","dense (Dense)                (None, 1000, 25)          4825      \n","=================================================================\n","Total params: 698,329\n","Trainable params: 697,465\n","Non-trainable params: 864\n","_________________________________________________________________\n","[INFO] The CRNN model was successfully created.\n","Epoch 1/36\n","40/40 [==============================] - 1062s 26s/step - loss: 2.7547 - accuracy: 0.1852 - val_loss: 3.0077 - val_accuracy: 0.1507\n","Epoch 2/36\n","40/40 [==============================] - 1068s 27s/step - loss: 2.5946 - accuracy: 0.2099 - val_loss: 3.8837 - val_accuracy: 0.1507\n","Epoch 3/36\n","40/40 [==============================] - 1084s 27s/step - loss: 2.5396 - accuracy: 0.2436 - val_loss: 3.3592 - val_accuracy: 0.1512\n","Epoch 4/36\n","40/40 [==============================] - 1051s 26s/step - loss: 2.4104 - accuracy: 0.2832 - val_loss: 2.6825 - val_accuracy: 0.2303\n","Epoch 5/36\n","40/40 [==============================] - 1050s 26s/step - loss: 2.2209 - accuracy: 0.3331 - val_loss: 2.4328 - val_accuracy: 0.3020\n","Epoch 6/36\n","40/40 [==============================] - 1047s 26s/step - loss: 2.0085 - accuracy: 0.3897 - val_loss: 2.5603 - val_accuracy: 0.2271\n","Epoch 7/36\n","40/40 [==============================] - 1038s 26s/step - loss: 1.8831 - accuracy: 0.4293 - val_loss: 2.5778 - val_accuracy: 0.2255\n","Epoch 8/36\n","40/40 [==============================] - 1041s 26s/step - loss: 1.6819 - accuracy: 0.4904 - val_loss: 2.8080 - val_accuracy: 0.2072\n","Epoch 9/36\n","40/40 [==============================] - 1041s 26s/step - loss: 1.5181 - accuracy: 0.5367 - val_loss: 2.2983 - val_accuracy: 0.3141\n","Epoch 10/36\n","40/40 [==============================] - 1039s 26s/step - loss: 1.3959 - accuracy: 0.5710 - val_loss: 2.2612 - val_accuracy: 0.3211\n","Epoch 11/36\n","40/40 [==============================] - 1042s 26s/step - loss: 1.2999 - accuracy: 0.5987 - val_loss: 1.9099 - val_accuracy: 0.4205\n","Epoch 12/36\n","40/40 [==============================] - 1042s 26s/step - loss: 1.2267 - accuracy: 0.6224 - val_loss: 1.7871 - val_accuracy: 0.4613\n","Epoch 13/36\n","40/40 [==============================] - 1043s 26s/step - loss: 1.1039 - accuracy: 0.6642 - val_loss: 1.6300 - val_accuracy: 0.5073\n","Epoch 14/36\n","40/40 [==============================] - 1042s 26s/step - loss: 1.0410 - accuracy: 0.6809 - val_loss: 1.5195 - val_accuracy: 0.5370\n","Epoch 15/36\n","40/40 [==============================] - 1054s 26s/step - loss: 0.9590 - accuracy: 0.7013 - val_loss: 1.3980 - val_accuracy: 0.5765\n","Epoch 16/36\n","40/40 [==============================] - 1047s 26s/step - loss: 0.8665 - accuracy: 0.7355 - val_loss: 1.5412 - val_accuracy: 0.5321\n","Epoch 17/36\n","40/40 [==============================] - 1043s 26s/step - loss: 0.8154 - accuracy: 0.7513 - val_loss: 1.3708 - val_accuracy: 0.5845\n","Epoch 18/36\n","40/40 [==============================] - 1049s 26s/step - loss: 0.7316 - accuracy: 0.7770 - val_loss: 1.4231 - val_accuracy: 0.5816\n","Epoch 19/36\n","40/40 [==============================] - 1059s 27s/step - loss: 0.7031 - accuracy: 0.7846 - val_loss: 1.2719 - val_accuracy: 0.6129\n","Epoch 20/36\n","40/40 [==============================] - 1043s 26s/step - loss: 0.6520 - accuracy: 0.8031 - val_loss: 1.2511 - val_accuracy: 0.6220\n","Epoch 21/36\n","40/40 [==============================] - 1041s 26s/step - loss: 0.5902 - accuracy: 0.8216 - val_loss: 1.2941 - val_accuracy: 0.6110\n","Epoch 22/36\n","40/40 [==============================] - 1045s 26s/step - loss: 0.5322 - accuracy: 0.8382 - val_loss: 1.2098 - val_accuracy: 0.6324\n","Epoch 23/36\n","40/40 [==============================] - 1043s 26s/step - loss: 0.4999 - accuracy: 0.8507 - val_loss: 1.2498 - val_accuracy: 0.6252\n","Epoch 24/36\n","40/40 [==============================] - 1045s 26s/step - loss: 0.4567 - accuracy: 0.8641 - val_loss: 1.2248 - val_accuracy: 0.6376\n","Epoch 25/36\n","40/40 [==============================] - 1046s 26s/step - loss: 0.4205 - accuracy: 0.8745 - val_loss: 1.2751 - val_accuracy: 0.6345\n","Epoch 26/36\n","40/40 [==============================] - 1043s 26s/step - loss: 0.3798 - accuracy: 0.8876 - val_loss: 1.2323 - val_accuracy: 0.6366\n","Epoch 27/36\n","40/40 [==============================] - 1043s 26s/step - loss: 0.3859 - accuracy: 0.8837 - val_loss: 1.2400 - val_accuracy: 0.6465\n","Epoch 28/36\n","40/40 [==============================] - 1042s 26s/step - loss: 0.3514 - accuracy: 0.8953 - val_loss: 1.2060 - val_accuracy: 0.6515\n","Epoch 29/36\n","40/40 [==============================] - 1042s 26s/step - loss: 0.3008 - accuracy: 0.9115 - val_loss: 1.2231 - val_accuracy: 0.6490\n","Epoch 30/36\n","40/40 [==============================] - 1041s 26s/step - loss: 0.2797 - accuracy: 0.9167 - val_loss: 1.2426 - val_accuracy: 0.6505\n","Epoch 31/36\n","40/40 [==============================] - 1041s 26s/step - loss: 0.2697 - accuracy: 0.9203 - val_loss: 1.2180 - val_accuracy: 0.6554\n","Epoch 32/36\n","40/40 [==============================] - 1045s 26s/step - loss: 0.2404 - accuracy: 0.9302 - val_loss: 1.2262 - val_accuracy: 0.6538\n","Epoch 33/36\n","40/40 [==============================] - 1045s 26s/step - loss: 0.2338 - accuracy: 0.9299 - val_loss: 1.2260 - val_accuracy: 0.6550\n","Epoch 34/36\n","40/40 [==============================] - 1043s 26s/step - loss: 0.2170 - accuracy: 0.9365 - val_loss: 1.2974 - val_accuracy: 0.6510\n","Epoch 35/36\n","40/40 [==============================] - 1045s 26s/step - loss: 0.2034 - accuracy: 0.9414 - val_loss: 1.2307 - val_accuracy: 0.6554\n","Epoch 36/36\n","40/40 [==============================] - 1045s 26s/step - loss: 0.1876 - accuracy: 0.9447 - val_loss: 1.2689 - val_accuracy: 0.6535\n","[INFO] The CRNN model was successfully trained.\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["17/17 - 91s - loss: 1.2689 - accuracy: 0.6535\n","\n"," Dev accuracy:  65.35 % \n","\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 2 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"gkdSbh6CUKn6"},"source":["### *shifted Log Mel - 62.06%*\n","*shifted log mel with stft length of 2^13 -> more than 63%*\n","\n","*shifted log mel with stft length of 2^12 -> more than 65%*\n","\n","*shifted log mel with stft length of 2^11 -> more than 64%*\n","\n","*shifted log mel with stft length of 2^10 -> more than 61%*"]},{"cell_type":"code","metadata":{"trusted":true,"id":"IwehQjp6UWke","outputId":"db44c06f-8378-4d12-8c3e-4daaf5c3179d"},"source":["#!/usr/bin/env python3\n","# Prepare data\n","x, y = IsophonicsDataset.load_preprocessed_dataset(\"../input/acrisophonics/Isophonics1000_log_mel.ds\")\n","n_samples, n_frames, n_chromas = x.shape\n","x = x.reshape((n_samples,n_frames,n_chromas,1))\n","train_x, dev_x, train_y, dev_y = sklearn.model_selection.train_test_split(x, y, test_size=0.3, random_state=args.seed)\n"," \n","# Create model\n","crnn_model = CRNN_1(input_shape=(n_frames, n_chromas, 1), output_classes=25)\n"," \n","# Train model\n","crnn_model.fit(train_x, train_y, dev_x, dev_y, epochs=100)\n"," \n","# Display score\n","crnn_model.display_training_progress()\n","print(\"\\n\",\"Dev accuracy: \", \"{:.2f}\".format(100*crnn_model.score(dev_x, dev_y)), \"%\", \"\\n\")\n","crnn_model.display_confusion_matrix(dev_x, dev_y)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["[INFO] The Preprocessed Isophonics Dataset was loaded successfully.\n","Model: \"sequential\"\n","_________________________________________________________________\n","Layer (type)                 Output Shape              Param #   \n","=================================================================\n","conv2d (Conv2D)              (None, 1000, 128, 16)     160       \n","_________________________________________________________________\n","batch_normalization (BatchNo (None, 1000, 128, 16)     64        \n","_________________________________________________________________\n","conv2d_1 (Conv2D)            (None, 1000, 128, 16)     2320      \n","_________________________________________________________________\n","batch_normalization_1 (Batch (None, 1000, 128, 16)     64        \n","_________________________________________________________________\n","conv2d_2 (Conv2D)            (None, 1000, 128, 16)     2320      \n","_________________________________________________________________\n","batch_normalization_2 (Batch (None, 1000, 128, 16)     64        \n","_________________________________________________________________\n","max_pooling2d (MaxPooling2D) (None, 1000, 43, 16)      0         \n","_________________________________________________________________\n","conv2d_3 (Conv2D)            (None, 1000, 43, 32)      4640      \n","_________________________________________________________________\n","batch_normalization_3 (Batch (None, 1000, 43, 32)      128       \n","_________________________________________________________________\n","conv2d_4 (Conv2D)            (None, 1000, 43, 32)      9248      \n","_________________________________________________________________\n","batch_normalization_4 (Batch (None, 1000, 43, 32)      128       \n","_________________________________________________________________\n","conv2d_5 (Conv2D)            (None, 1000, 43, 32)      9248      \n","_________________________________________________________________\n","batch_normalization_5 (Batch (None, 1000, 43, 32)      128       \n","_________________________________________________________________\n","max_pooling2d_1 (MaxPooling2 (None, 1000, 15, 32)      0         \n","_________________________________________________________________\n","conv2d_6 (Conv2D)            (None, 1000, 15, 64)      18496     \n","_________________________________________________________________\n","batch_normalization_6 (Batch (None, 1000, 15, 64)      256       \n","_________________________________________________________________\n","conv2d_7 (Conv2D)            (None, 1000, 15, 64)      36928     \n","_________________________________________________________________\n","batch_normalization_7 (Batch (None, 1000, 15, 64)      256       \n","_________________________________________________________________\n","max_pooling2d_2 (MaxPooling2 (None, 1000, 4, 64)       0         \n","_________________________________________________________________\n","conv2d_8 (Conv2D)            (None, 1000, 4, 80)       46160     \n","_________________________________________________________________\n","batch_normalization_8 (Batch (None, 1000, 4, 80)       320       \n","_________________________________________________________________\n","conv2d_9 (Conv2D)            (None, 1000, 4, 80)       57680     \n","_________________________________________________________________\n","batch_normalization_9 (Batch (None, 1000, 4, 80)       320       \n","_________________________________________________________________\n","reshape (Reshape)            (None, 1000, 320)         0         \n","_________________________________________________________________\n","bidirectional (Bidirectional (None, 1000, 192)         320256    \n","_________________________________________________________________\n","dense (Dense)                (None, 1000, 25)          4825      \n","=================================================================\n","Total params: 514,009\n","Trainable params: 513,145\n","Non-trainable params: 864\n","_________________________________________________________________\n","[INFO] The CRNN model was successfully created.\n","Epoch 1/100\n","40/40 [==============================] - 23s 387ms/step - loss: 2.4630 - accuracy: 0.2702 - val_loss: 2.2191 - val_accuracy: 0.3138\n","Epoch 2/100\n","40/40 [==============================] - 13s 326ms/step - loss: 2.1959 - accuracy: 0.3172 - val_loss: 2.1988 - val_accuracy: 0.3193\n","Epoch 3/100\n","40/40 [==============================] - 13s 325ms/step - loss: 2.1961 - accuracy: 0.3128 - val_loss: 2.1626 - val_accuracy: 0.3261\n","Epoch 4/100\n","40/40 [==============================] - 13s 327ms/step - loss: 2.1561 - accuracy: 0.3247 - val_loss: 2.1593 - val_accuracy: 0.3239\n","Epoch 5/100\n","40/40 [==============================] - 13s 325ms/step - loss: 2.1338 - accuracy: 0.3370 - val_loss: 2.1410 - val_accuracy: 0.3273\n","Epoch 6/100\n","40/40 [==============================] - 13s 326ms/step - loss: 2.1140 - accuracy: 0.3358 - val_loss: 2.0942 - val_accuracy: 0.3316\n","Epoch 7/100\n","40/40 [==============================] - 13s 327ms/step - loss: 2.0913 - accuracy: 0.3425 - val_loss: 2.0830 - val_accuracy: 0.3397\n","Epoch 8/100\n","40/40 [==============================] - 13s 325ms/step - loss: 1.9858 - accuracy: 0.3738 - val_loss: 1.9950 - val_accuracy: 0.3579\n","Epoch 9/100\n","40/40 [==============================] - 13s 326ms/step - loss: 1.9662 - accuracy: 0.3852 - val_loss: 1.9697 - val_accuracy: 0.3754\n","Epoch 10/100\n","40/40 [==============================] - 13s 328ms/step - loss: 1.8338 - accuracy: 0.4259 - val_loss: 1.9394 - val_accuracy: 0.3867\n","Epoch 11/100\n","40/40 [==============================] - 13s 326ms/step - loss: 1.7717 - accuracy: 0.4441 - val_loss: 1.9062 - val_accuracy: 0.3985\n","Epoch 12/100\n","40/40 [==============================] - 13s 327ms/step - loss: 1.7471 - accuracy: 0.4547 - val_loss: 1.8760 - val_accuracy: 0.4240\n","Epoch 13/100\n","40/40 [==============================] - 13s 325ms/step - loss: 1.6448 - accuracy: 0.4905 - val_loss: 1.7464 - val_accuracy: 0.4498\n","Epoch 14/100\n","40/40 [==============================] - 13s 326ms/step - loss: 1.5849 - accuracy: 0.5015 - val_loss: 1.8371 - val_accuracy: 0.4350\n","Epoch 15/100\n","40/40 [==============================] - 13s 325ms/step - loss: 1.5716 - accuracy: 0.5103 - val_loss: 1.7147 - val_accuracy: 0.4625\n","Epoch 16/100\n","40/40 [==============================] - 13s 325ms/step - loss: 1.4750 - accuracy: 0.5372 - val_loss: 1.7971 - val_accuracy: 0.4506\n","Epoch 17/100\n","40/40 [==============================] - 13s 327ms/step - loss: 1.4786 - accuracy: 0.5370 - val_loss: 1.6054 - val_accuracy: 0.5027\n","Epoch 18/100\n","40/40 [==============================] - 13s 325ms/step - loss: 1.3938 - accuracy: 0.5579 - val_loss: 1.7887 - val_accuracy: 0.4571\n","Epoch 19/100\n","40/40 [==============================] - 13s 326ms/step - loss: 1.3506 - accuracy: 0.5714 - val_loss: 1.5679 - val_accuracy: 0.5230\n","Epoch 20/100\n","40/40 [==============================] - 13s 325ms/step - loss: 1.3199 - accuracy: 0.5847 - val_loss: 1.4896 - val_accuracy: 0.5399\n","Epoch 21/100\n","40/40 [==============================] - 13s 326ms/step - loss: 1.2153 - accuracy: 0.6113 - val_loss: 1.5526 - val_accuracy: 0.5233\n","Epoch 22/100\n","40/40 [==============================] - 13s 325ms/step - loss: 1.1832 - accuracy: 0.6220 - val_loss: 1.4858 - val_accuracy: 0.5378\n","Epoch 23/100\n","40/40 [==============================] - 13s 324ms/step - loss: 1.1509 - accuracy: 0.6337 - val_loss: 1.4486 - val_accuracy: 0.5487\n","Epoch 24/100\n","40/40 [==============================] - 13s 326ms/step - loss: 1.1155 - accuracy: 0.6415 - val_loss: 1.6129 - val_accuracy: 0.4854\n","Epoch 25/100\n","40/40 [==============================] - 13s 326ms/step - loss: 1.0898 - accuracy: 0.6501 - val_loss: 1.4770 - val_accuracy: 0.5533\n","Epoch 26/100\n","40/40 [==============================] - 13s 326ms/step - loss: 1.0362 - accuracy: 0.6660 - val_loss: 1.5132 - val_accuracy: 0.5406\n","Epoch 27/100\n","40/40 [==============================] - 13s 326ms/step - loss: 1.0046 - accuracy: 0.6738 - val_loss: 1.4382 - val_accuracy: 0.5528\n","Epoch 28/100\n","40/40 [==============================] - 13s 326ms/step - loss: 0.9764 - accuracy: 0.6855 - val_loss: 1.3830 - val_accuracy: 0.5805\n","Epoch 29/100\n","40/40 [==============================] - 13s 326ms/step - loss: 0.9096 - accuracy: 0.7037 - val_loss: 1.4005 - val_accuracy: 0.5677\n","Epoch 30/100\n"],"name":"stdout"},{"output_type":"stream","text":["40/40 [==============================] - 13s 326ms/step - loss: 0.9083 - accuracy: 0.7039 - val_loss: 1.3939 - val_accuracy: 0.5752\n","Epoch 31/100\n","40/40 [==============================] - 13s 327ms/step - loss: 0.8839 - accuracy: 0.7135 - val_loss: 1.4083 - val_accuracy: 0.5753\n","Epoch 32/100\n","40/40 [==============================] - 13s 326ms/step - loss: 0.8165 - accuracy: 0.7335 - val_loss: 1.4563 - val_accuracy: 0.5630\n","Epoch 33/100\n","40/40 [==============================] - 13s 326ms/step - loss: 0.8059 - accuracy: 0.7357 - val_loss: 1.3892 - val_accuracy: 0.5855\n","Epoch 34/100\n","40/40 [==============================] - 13s 327ms/step - loss: 0.7777 - accuracy: 0.7473 - val_loss: 1.3659 - val_accuracy: 0.5932\n","Epoch 35/100\n","40/40 [==============================] - 13s 327ms/step - loss: 0.7300 - accuracy: 0.7632 - val_loss: 1.4596 - val_accuracy: 0.5542\n","Epoch 36/100\n","40/40 [==============================] - 13s 326ms/step - loss: 0.7915 - accuracy: 0.7410 - val_loss: 1.3677 - val_accuracy: 0.5862\n","Epoch 37/100\n","40/40 [==============================] - 13s 327ms/step - loss: 0.7322 - accuracy: 0.7598 - val_loss: 1.3613 - val_accuracy: 0.5903\n","Epoch 38/100\n","40/40 [==============================] - 13s 326ms/step - loss: 0.6697 - accuracy: 0.7808 - val_loss: 1.3618 - val_accuracy: 0.6003\n","Epoch 39/100\n","40/40 [==============================] - 13s 327ms/step - loss: 0.6402 - accuracy: 0.7901 - val_loss: 1.3756 - val_accuracy: 0.5914\n","Epoch 40/100\n","40/40 [==============================] - 13s 328ms/step - loss: 0.6002 - accuracy: 0.8026 - val_loss: 1.3773 - val_accuracy: 0.5968\n","Epoch 41/100\n","40/40 [==============================] - 13s 327ms/step - loss: 0.5979 - accuracy: 0.8059 - val_loss: 1.3611 - val_accuracy: 0.5964\n","Epoch 42/100\n","40/40 [==============================] - 13s 326ms/step - loss: 0.5535 - accuracy: 0.8186 - val_loss: 1.3769 - val_accuracy: 0.5983\n","Epoch 43/100\n","40/40 [==============================] - 13s 326ms/step - loss: 0.5628 - accuracy: 0.8146 - val_loss: 1.4264 - val_accuracy: 0.5832\n","Epoch 44/100\n","40/40 [==============================] - 13s 327ms/step - loss: 0.5082 - accuracy: 0.8351 - val_loss: 1.4302 - val_accuracy: 0.5958\n","Epoch 45/100\n","40/40 [==============================] - 13s 326ms/step - loss: 0.3957 - accuracy: 0.8695 - val_loss: 1.4005 - val_accuracy: 0.6156\n","Epoch 53/100\n","40/40 [==============================] - 13s 324ms/step - loss: 0.4143 - accuracy: 0.8619 - val_loss: 1.4555 - val_accuracy: 0.6013\n","Epoch 54/100\n","40/40 [==============================] - 13s 325ms/step - loss: 0.3841 - accuracy: 0.8722 - val_loss: 1.4448 - val_accuracy: 0.6000\n","Epoch 55/100\n","40/40 [==============================] - 13s 325ms/step - loss: 0.3678 - accuracy: 0.8788 - val_loss: 1.4369 - val_accuracy: 0.6113\n","Epoch 56/100\n","40/40 [==============================] - 13s 325ms/step - loss: 0.3413 - accuracy: 0.8873 - val_loss: 1.4587 - val_accuracy: 0.6094\n","Epoch 57/100\n","40/40 [==============================] - 13s 324ms/step - loss: 0.3588 - accuracy: 0.8808 - val_loss: 1.4802 - val_accuracy: 0.6049\n","Epoch 58/100\n","40/40 [==============================] - 13s 326ms/step - loss: 0.3374 - accuracy: 0.8888 - val_loss: 1.4899 - val_accuracy: 0.6064\n","Epoch 59/100\n","40/40 [==============================] - 13s 325ms/step - loss: 0.3076 - accuracy: 0.8988 - val_loss: 1.4630 - val_accuracy: 0.6115\n","Epoch 60/100\n","40/40 [==============================] - 13s 326ms/step - loss: 0.2967 - accuracy: 0.9020 - val_loss: 1.5380 - val_accuracy: 0.6100\n","Epoch 61/100\n","40/40 [==============================] - 13s 326ms/step - loss: 0.2722 - accuracy: 0.9101 - val_loss: 1.4604 - val_accuracy: 0.6161\n","Epoch 62/100\n","40/40 [==============================] - 13s 327ms/step - loss: 0.2570 - accuracy: 0.9171 - val_loss: 1.4918 - val_accuracy: 0.6154\n","Epoch 63/100\n","40/40 [==============================] - 13s 326ms/step - loss: 0.2404 - accuracy: 0.9221 - val_loss: 1.5086 - val_accuracy: 0.6136\n","Epoch 64/100\n","40/40 [==============================] - 13s 326ms/step - loss: 0.2350 - accuracy: 0.9241 - val_loss: 1.5093 - val_accuracy: 0.6199\n","Epoch 65/100\n","40/40 [==============================] - 13s 327ms/step - loss: 0.2554 - accuracy: 0.9145 - val_loss: 1.5297 - val_accuracy: 0.6041\n","Epoch 66/100\n","40/40 [==============================] - 13s 325ms/step - loss: 0.2621 - accuracy: 0.9141 - val_loss: 1.5261 - val_accuracy: 0.6146\n","Epoch 67/100\n","40/40 [==============================] - 13s 327ms/step - loss: 0.2349 - accuracy: 0.9224 - val_loss: 1.5391 - val_accuracy: 0.6179\n","Epoch 68/100\n","40/40 [==============================] - 13s 326ms/step - loss: 0.2188 - accuracy: 0.9283 - val_loss: 1.5670 - val_accuracy: 0.6131\n","Epoch 69/100\n","40/40 [==============================] - 13s 327ms/step - loss: 0.2163 - accuracy: 0.9287 - val_loss: 1.5906 - val_accuracy: 0.6080\n","Epoch 70/100\n","40/40 [==============================] - 13s 327ms/step - loss: 0.2201 - accuracy: 0.9266 - val_loss: 1.5873 - val_accuracy: 0.6079\n","Epoch 71/100\n","40/40 [==============================] - 13s 326ms/step - loss: 0.2159 - accuracy: 0.9283 - val_loss: 1.5677 - val_accuracy: 0.6105\n","Epoch 72/100\n","40/40 [==============================] - 13s 327ms/step - loss: 0.1944 - accuracy: 0.9363 - val_loss: 1.5783 - val_accuracy: 0.6184\n","Epoch 73/100\n","40/40 [==============================] - 13s 326ms/step - loss: 0.2014 - accuracy: 0.9329 - val_loss: 1.6432 - val_accuracy: 0.6072\n","Epoch 74/100\n","40/40 [==============================] - 13s 327ms/step - loss: 0.1928 - accuracy: 0.9361 - val_loss: 1.6126 - val_accuracy: 0.6134\n","Epoch 75/100\n","40/40 [==============================] - 13s 326ms/step - loss: 0.1787 - accuracy: 0.9410 - val_loss: 1.6030 - val_accuracy: 0.6191\n","Epoch 76/100\n","40/40 [==============================] - 13s 327ms/step - loss: 0.1621 - accuracy: 0.9468 - val_loss: 1.6065 - val_accuracy: 0.6187\n","Epoch 77/100\n","40/40 [==============================] - 13s 327ms/step - loss: 0.1579 - accuracy: 0.9480 - val_loss: 1.6300 - val_accuracy: 0.6225\n","Epoch 78/100\n","40/40 [==============================] - 13s 327ms/step - loss: 0.1503 - accuracy: 0.9501 - val_loss: 1.6158 - val_accuracy: 0.6245\n","Epoch 79/100\n","40/40 [==============================] - 13s 327ms/step - loss: 0.1500 - accuracy: 0.9508 - val_loss: 1.6614 - val_accuracy: 0.6167\n","Epoch 80/100\n","40/40 [==============================] - 13s 325ms/step - loss: 0.1433 - accuracy: 0.9533 - val_loss: 1.6709 - val_accuracy: 0.6227\n","Epoch 81/100\n","40/40 [==============================] - 13s 326ms/step - loss: 0.1435 - accuracy: 0.9530 - val_loss: 1.6513 - val_accuracy: 0.6179\n","Epoch 82/100\n","40/40 [==============================] - 13s 325ms/step - loss: 0.1783 - accuracy: 0.9392 - val_loss: 1.7373 - val_accuracy: 0.6007\n","Epoch 83/100\n","40/40 [==============================] - 13s 326ms/step - loss: 0.2155 - accuracy: 0.9246 - val_loss: 1.6797 - val_accuracy: 0.5990\n","Epoch 84/100\n","40/40 [==============================] - 13s 326ms/step - loss: 0.2296 - accuracy: 0.9211 - val_loss: 1.7331 - val_accuracy: 0.5961\n","Epoch 85/100\n","40/40 [==============================] - 13s 326ms/step - loss: 0.2663 - accuracy: 0.9101 - val_loss: 1.6746 - val_accuracy: 0.6049\n","Epoch 86/100\n","40/40 [==============================] - 13s 325ms/step - loss: 0.2235 - accuracy: 0.9235 - val_loss: 1.6554 - val_accuracy: 0.6123\n","Epoch 87/100\n","40/40 [==============================] - 13s 325ms/step - loss: 0.1846 - accuracy: 0.9366 - val_loss: 1.6957 - val_accuracy: 0.6097\n","Epoch 88/100\n","40/40 [==============================] - 13s 327ms/step - loss: 0.1464 - accuracy: 0.9509 - val_loss: 1.6523 - val_accuracy: 0.6135\n","Epoch 89/100\n","40/40 [==============================] - 13s 326ms/step - loss: 0.1410 - accuracy: 0.9531 - val_loss: 1.6986 - val_accuracy: 0.6145\n","Epoch 90/100\n","40/40 [==============================] - 13s 325ms/step - loss: 0.1551 - accuracy: 0.9476 - val_loss: 1.6907 - val_accuracy: 0.6110\n","Epoch 91/100\n","40/40 [==============================] - 13s 326ms/step - loss: 0.1425 - accuracy: 0.9519 - val_loss: 1.7199 - val_accuracy: 0.6076\n","Epoch 92/100\n","40/40 [==============================] - 13s 326ms/step - loss: 0.1267 - accuracy: 0.9568 - val_loss: 1.7320 - val_accuracy: 0.6131\n","Epoch 93/100\n"],"name":"stdout"},{"output_type":"stream","text":["40/40 [==============================] - 13s 325ms/step - loss: 0.1216 - accuracy: 0.9590 - val_loss: 1.7503 - val_accuracy: 0.6165\n","Epoch 94/100\n","40/40 [==============================] - 13s 330ms/step - loss: 0.1191 - accuracy: 0.9607 - val_loss: 1.7301 - val_accuracy: 0.6227\n","Epoch 95/100\n","40/40 [==============================] - 13s 326ms/step - loss: 0.1028 - accuracy: 0.9664 - val_loss: 1.7379 - val_accuracy: 0.6209\n","Epoch 96/100\n","40/40 [==============================] - 13s 325ms/step - loss: 0.0984 - accuracy: 0.9678 - val_loss: 1.7692 - val_accuracy: 0.6180\n","Epoch 97/100\n","40/40 [==============================] - 13s 326ms/step - loss: 0.1071 - accuracy: 0.9645 - val_loss: 1.7792 - val_accuracy: 0.6213\n","Epoch 98/100\n","40/40 [==============================] - 13s 326ms/step - loss: 0.1037 - accuracy: 0.9656 - val_loss: 1.7890 - val_accuracy: 0.6203\n","Epoch 99/100\n","40/40 [==============================] - 13s 326ms/step - loss: 0.1003 - accuracy: 0.9665 - val_loss: 1.7700 - val_accuracy: 0.6198\n","Epoch 100/100\n","40/40 [==============================] - 13s 326ms/step - loss: 0.0977 - accuracy: 0.9672 - val_loss: 1.8034 - val_accuracy: 0.6206\n","[INFO] The CRNN model was successfully trained.\n"],"name":"stdout"},{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 Axes>"],"image/png":"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\n"},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["17/17 - 2s - loss: 1.8034 - accuracy: 0.6206\n","\n"," Dev accuracy:  62.06 % \n","\n"],"name":"stdout"},{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 2 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\n"},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"TJvW1NhTMuOo"},"source":["## Historical MLP progress"]},{"cell_type":"markdown","metadata":{"id":"KoT88AF7sVcs"},"source":["### MLP with scaler preprocessing, mel log - *50.68% (56.77% with another sample rate) - all songs in C major key*"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":410},"id":"3nRhyFpVr0cP","executionInfo":{"status":"ok","timestamp":1615053322778,"user_tz":-60,"elapsed":300255,"user":{"displayName":"Vojtech Lanz","photoUrl":"","userId":"15791523486540207166"}},"outputId":"eedff8d7-4a62-4cce-a627-c1853e2fdfcb"},"source":["#!/usr/bin/env python3\n","# hop length 11008, sample rate 22050, flattened, window size 5, normalized to C major, skip 1\n","# Prepare data\n","x, y = IsophonicsDataset.load_preprocessed_dataset(\"./PreprocessedDatasets/isophonics_22050_11008_5_flattened_mel_log_1skip.ds\")\n","train_x, dev_x, train_y, dev_y = sklearn.model_selection.train_test_split(x, y, test_size=args.test_size, random_state=args.seed)\n","\n","# Create model\n","mlp_model = MLP_scalered(random_state=7)\n","\n","# Train model\n","mlp_model.fit(train_x, train_y)\n","\n","# Display score\n","print(\"\\n\",\"Dev accuracy: \", \"{:.2f}\".format(100*mlp_model.score(dev_x, dev_y)), \"%\", \"\\n\")\n","mlp_model.display_confusion_matrix(dev_x, dev_y)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["[INFO] The Preprocessed Isophonics Dataset was loaded successfully.\n","[INFO] The MLP model with scaler preprocessing was successfully created.\n","[INFO] The MLP model was successfully trained.\n","\n"," Dev accuracy:  56.77 % \n","\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAVsAAAEjCAYAAACcvqJBAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nO29eZxlVXmv/3y7oAEhIDPIYGNAkSkMDYoDglPAHwEHuAzeG4kSTJQYzVWjublIiEM0GojCDSIiThEUBVFbMYJEQcRuEGiaQZsGpUWZGpkEurvq+/tjraJ3nTpnn33q7DpTv09/9qf3sPZa7zq166113v0Osk0QBEEwu8zptwBBEARrA6FsgyAIekAo2yAIgh4QyjYIgqAHhLINgiDoAaFsgyAIesA6/RZgUBjbaEOvs+lmpW3W/92Tpde9erxOkdYqNKf9331PTPRAkvay9EqOSkjt2wyQe+ejPPSA7S276eNPD9nQD66o9rt23U1PXWb70G7Gq4uRVLaSDPyb7f+dj98NbGT71Fb3rLPpZjzrf7+ztN/nfegXpdfHVzzUXri6Hvw5Y933MVHDH4cqv+wVmPOMZ7RtM/H447WM1Y45z9hwIOQA2n6+mju3bRdeubL9OHU8lxWehR9MfO1X3Q7zwIpxrr1s+0pt1932ji26Ha8uRtWM8BTwekkD80EHQVAXZtwTlbZBYlSV7WrgHOBd/RYkCIJ6MTCBK22DxKgqW4CzgDdK2qRVA0knSVokadF4L78aBkHQFRMV/w0SI6tsbT8CfAF4R0mbc2zPtz1/bMNyO10QBIOBMeOutg0SI6tsM2cAbwFCkwbBCBFmhAHD9grgqySFGwTBCGBgFROVtkFipJVt5hNAeCUEwYhgGEozwkj62dreSNI2JDPC/sCtwAGSnmu7qbPs+vev5nln31/a74LFV5ReP3TH+e1lW726bZsqaE73/pcTTz7VfqB2vrhq//e6nawAq+c/t22bOT++qbxBHX7DgHZ8VnmD25a276RHv+hzNli/bRtX8H+deLI8YKcSPVRug7VmrcZIKltJAi4GPm/72HzuT4CtgfLIhCAIBhpjxgfMHluFUTUjHAKssn325AnbN9r+cR9lCoKgDgzjFbd2SDpU0u2Slkp6X5PrB0m6XtJqSUc1ub6xpOWSzmw31kiubIE9gOv6LUQQBPVjxCq6DxOXNEbyx38VsBxYKOlS27cUmv0aOAF4d4tu/hn4UZXxRnVlW4liUMPK8T/0W5wgCCpgYMLVtjYcACy1vcz2SuAC4MgpY9l32b6JJmZiSfuRTJPfryL3qCrbJcB+7RoVgxrmjrVPhBIEwWAwjiptbdgOuLtwvDyfa4ukOSRPp1Yr3mmMqrK9AlhP0kmTJyTtJemlfZQpCIIaMB0p2y0mv73m7aQ23VflbcAC28ur3jCSNlvblvQ64AxJfw88CdwFlOdQDIJgKJhwZZvtA7Zb+WT+BtihcLx9PleFA4GXSnobsBEwV9Jjtqe9ZJtkJJUtgO17gP9Ruf1TTzH+iztK2/zpdvuUXtfcCjlma/KzbeevW5c/b1sq+LZWyXQ3579/XoMw9TDezo+2l87ybcYa//3DPRJkcJhc2dbAQmAXSTuRlOyxwPGVZLDfOLkv6QRgfpmihQEwI0jaRtIFku6QdJ2kBZLae7hP72e+pE/OhoxBEAwORqzyWKWttB97NXAycBkp8OmrtpdIOk3SEQCS9pe0HDga+LSkJTOVu68r2zqDD2wvAhbVLmQQBANFjStbbC8AFjScO6Wwv5BkXijr43zg/HZj9XtlWyn4QNJjkv5V0hJJP5B0gKQrJS0r/AU6WNK38/6pks4rtGmZZjEIgmFDjHtOpW2Q6Lc0LYMPJN1QONwQuML27sCjwAdJjsivA05r0feuwJ+SfOk+IGnduoQOgqB/pEoNcyptg8TAviCzvXfhcCXwvby/GHjK9ipJi4F5Lbr4ju2ngKck3UcyTUxx08huICcBrE/42QbBsFCXGaGX9FvZLgGmxRs3YZX99GvZCVJBR2xPSGo1h2JKq3GazNX2OaRaZWyszYYvs0UQrIXYGjgTQRX6LXEEHwRB0BEpefhYpW2Q6KuyzavV1wGvzK5fS4CPAL9rsNkGQRBkhvMFWb/NCGXBB3sX2mxU2D+14f6N8v9XAle2aLNHJWHaJVlu42Dupyok466QyLmnTvPBdOLzH2gmX5ANG8MncaZdMISkr0jaSdI7JR3XT1mDIKiXcavSNkgMpbItBENcafuPbe8HvJ/kcTDJPNt3Ai+jYr7JIAgGHyPGmVNpGyT6bkaYIU2DIQAkfRnYB9g22313Ab4j6Uzb5/ZF2iAIasPAKg+f6ho+iRMtgyFsv1HS0cCOwEXAx20f3UvhgiCYPczgmQiqMKzKth37ApcDewE3tmoUQQ1BMJwM4wuyYVW2TYMhJL0G+DCwE3A4sCXwuKRX2D6ksX0ENQTB8GEzcG5dVRg+iRNNgyFIeRP2A262vSdJKe/TTNEGQTCsiImK2yAxlMq2LBiC9HLsRklzgXVtP9JHUYMgqBlDBDU0Q9I2wBnA/sDvgXuBd9r+Rb7+FeAfSFUt77X9lSr9NguGkLQAON722/KpgzoSthfO7FXGmFMhzLBChYQgGEUmk4cPG7OqbCsmB59n+05JLyNlTZ8xtl/Tzf1BEAwHg+ZDW4XZXtnW4g8r6Xzgidx+K+DNwJ+Tiq5da/uE3O4uYD6pANt3gauAF5HqCx1p+4nZmmgQBL3BwMSAmQiqMNsSl/rDAh8gJQI/klQWeO9JRVusA5TZlKRc3wVcCpwO7A7sKWlvprMLcFZOOP574A31TCkIgv5SrYz5oOW87bfrV0t/2GIdoMy3conyxSTb7mKA/HJsHtCYJexO25PnrqNJkvHwsw2C4SNWts1ZQnLFmoKk12TTwdtIK9TPAH8h6YclfU2m1JpgamLwCZr/0aiUPNz2fNvz12W90okEQTA41LWylXSopNslLZU0rRS5pIMkXS9ptaSjCuf3lnRNrot4k6Rj2o0128o2/GGDIKgVW6yaWKfSVoakMeAs4DBgN+A4Sbs1NPs1cALwnw3n/wD8eTZTHgqcIemZZePNqrLtxh+2ic02CIIg57OtJajhAGCp7WW2VwIXkN4frRnLvsv2TaRv0MXzv7D9y7x/D3AfKWK1JbNusy1JDg6wKP8/zR+2oXb7CYX9u0gv3ppdm5d3H2ho8/FO5Q6CYFCprQbZdsDdhePlwAs6lkY6AJgL3FHWrt8vyGYFSeOkKrzrAquBLwCn254ouQmtO7e845LbAbx6dYeStqBCwMLY7s8rvT5+69JaxqmFCtUpNNbeSb22z7cN7Z4Dr1rZEzmAtp+d1lm3bRc9lbcHpBdklT0NtpC0qHB8Ts6JUguStgW+CLypVL8wosoWeGKyFLqkrUj2lo1JrmZBEAw5HQQ1PGB7fotrvwF2KBxvn89VQtLGwHeA/2P7p+3aD5//RIfYvo/k3nVyjmgLgmCIMWK1xyptbVgI7JLLZ80FjiX58Lclt78Y+ILti6rcM/LKFsD2MmCMFH0WBMEQk1Isdl+DzPZqUoqAy4Bbga/aXlJ8OS9pf0nLgaOBT+eX/JDeQx0EnCDphrw1C656mlE1I1QighqCYDjpwGZbiu0FwIKGc8WX8wtJ5oXG+74EfKmTsdYKZSvpOaTAhvuK56ckD5+zeSQPD4IhwGgoI8hGXtlK2hI4Gzgz+/0GQTDkDFregyqMqrLdIIcDT7p+fRH4t/6KFARBHXTo+jUwjKSytTvPLCwJzW3js9hmYdwrP1CAidtL/adZ+ap92vYx97JFbdu0pSYHjzkbbdi2zfjvH65lrHZo3fJfi0HyW9VY+6/TXl3hZzRUX/pUxdNg4Oirsp1R8EF5f0cAu9n+l/qkDIJgkJj0Rhg2+r2yrTX4wPalVPSTC4JgeBnGF2QDI3FZ8IGkeZJuk3S+pF9I+rKkV0q6WtIvc2wykk6QdGbeP1/SJyX9RNKyYnq0IAiGl+SNUG0bJAZG2cLU4ANJz8oFHCfZGfgEsGvejgdeArybVDCyGdvmNocDYVoIghFhGEuZ99uM0JKcLaxYwPHOhuoMlxcqN8xr0c0l2f57i6StGy9OCWpQ+xc0QRD0n/BGqIFWwQeZxuoMxcoNreZRvGfaT6cY1LDJ2BbD9Do2CNZeLFZPhDfCjInggyAIqjCZPHzY6LeybRl8IOlZwLm2X1NyfxAEayFhRuiQsuCDos22WJ2h4Ju7v6T/S/LN3Su3Ox84P3fxbODbks4ALrS9UaksExNMPP54ucBzBuerS7sAiioBC2Obb9a2zfiDK9oIUs+XkPFHHqulnzqY+MMf+i3CGtp8vhNPPtm+jyrPrXuUSL4GwmbbO9r65kraAJiw/aSk/YH39EXSIAhmhWFUtgPl+tUpzXxzczn0xcAe2VNhT2ChpDBHBMEIkJKHz6m0DRLDuLKdgu1luSTxVsC9tg+R9B5gGanw4+G2Y2UbBKOCY2U7SOwL3Eiy5d7YqpGkkyQtkrRo1RQvsSAIBpVJm+2wRZAN/cq26Jsr6URSmYudgecDOwL3SjrM9hsb752SPFybhbtZEAwJg6ZIqzDUyraJb+65ki4luYwdIelntg/or5RBENTJZG6EYWMYlW27xOAHAVdJ2gH4VR/kC4JglnEo29mnXWLwhrLCR8+yOEEQ9Bib2jwNJB0K/DspAda5jbmwJR0EnEF6/3NsUb9IehPwj/nwg7Y/XzbWrCnbqonBs6vWYaSsXBfavqaLMX9i+0Uzl7oNE8Pj+F2FtgEL0L4SQ12R1SP22Q4UI/jZ1rGyzV5MZwGvApaTXEQvtX1LodmvgRNI2QWL925G8u2fT3pnd12+96FW482mN8ITtve2vTtpMofRkBS8GHwA7A90VadlVhVtEAQDQm35bA8AltpeZnslcAFwZLGB7bts30RKeFXkT4H/sr0iK9j/Ag4tG6wnrl/dBh9IulLS6dlN61ZJ+0v6Rk4c/sFCu8fy/wfney7KSce/3JiQPAiC4cVWpa0N2wF3F46X53NV6PjentlsOw0+kHQucLbtydXuStvzJf0t8E1gP2AFcIek020/2DDkPsDuwD3A1cCLgatmcYpBEPSADnMjbCGp+I35nOzy2XP6/YJsX+BikolhSvCB7RMb2k7WFlsMLLH9WwBJy4AdgEZl+zPby3ObG0gJxqco2ynJw3lGl1MJgqAnuKNXBQ/Ynt/i2m9IumOS7fO5KvwGOLjh3ivLbuiZsu0m+CBTTBbemEi82TyKbcabtYmghiAYPgyM1+ONsBDYRdJOJOV5LKncVhUuAz4sadN8/Grg/WU39MRm2xh8YPvcLNwVOYPXUtvPL1G0QRAEmXpekNleTVr0XQbcCnzV9hJJp0k6AiC/H1pOciP9dC7Jhe0VwD+TFPZC4LR8riWzubLtKvigic02CIIAqM/j0PYCYEHDuVMK+wtJJoJm954HnFd1rFlTtt0GHxRttrYPLuxfScE20nBtoxZtTq4kdLfJwXvpz1iHc0WVJ7ZNm3W2mVZHcxqr721WUm4GRLWkaWjduW3beNXKHkjSWyKCbIAoBFVMckFjdEgQBMOHHcp20Hi6okMQBKPF+EQo2yAIglknVraDxeQLukk+YvvCvkkTBEEtmErRYQPHKCvbtmaECGoIguFkGF+VjrKybUsENQTBEBIvyIIgCHrEEC6NWipbSZ+iZEq23zErEtVHo832e7bf1zdpgiCojYkR80YY6sitdkEVM0Hrln8R8FM9DGpo5+Dfo4ySVQIWxrbasm2biQqJzL16dSWZuqZdcMsAJeP2+ODI0ivMiJkRGks8SHqG7T/ULUCdwQeSFgDH2/59LcIFQTB4GBglZTuJpAOBzwIbATtK+hPgrbbfVpMMtQUf2J6WeDwIgtFjGCO3q2T9OoNUAuJBANs3kpLI9AxJ50v6D0k/lbQsV2I4L1dtOL/Q7i5JW0ial699RtISSd/PJXiCIBgFXHEbICqlWLR9d8OpOg1FG0i6obAdA1BMc5bZFDgQeBcpkfjppEoMe0pqtjLeBTgr10D7PfCGxgaSTsqldhatmpL+NgiCwUV4oto2SFRx/bpb0osAS1oX+FtS7se6aGpGKKY5y3zLtnO9snttLwbI+SXnATc0tL/T9uS563KbxjHCzzYIho0h9bOtsrL9K+DtpGJm9wB75+NeU3ulhiAIhpQhNCO0VUC2HwCigkIQBAPECK5sJT1H0rck3S/pPknfzPXE6qLRZvsvedxGm20QBEFiFFe2wH8CZwGvy8fHAl8BXlCHAK2CDxpKU5xQ2L8L2KPFtXl59wFgj0YfXknn5/unoXXXYZ0ty53vx7dv45y/cHH59RrROm1+dGMVYjoqOMTXEUgwfn9j4ePpLP/arm3bbH/0beUNago2GNty89Lr43VVnqiBORu2T6DkJ55o36ZXASN1MWCKtApVlO0zbH+xcPwlSe+ZLYFqJhKIB8GoYQbO06AKLc0IkjaTtBnwXUnvy76rz5b0XhoKpAVBEPSUmswIkg6VdLukpZKm5U6RtJ6kC/P1ayXNy+fXlfR5SYuzT39pGXMoX9lel8Wd/BPy1sI106ZG+oBQTEZzp+3XlbYOgmA4qMH1S9IYyUT6KmA5sFDSpbZvKTR7C/CQ7Z0lHQt8FDiGVKR2Pdt7SnoGcIukr7QyU0J5boSdup5N/yk1I0xJHj62Uc+ECoKgO1SPzfYAYKntZQCSLgCOBIrK9kjg1Lx/EXCmJJEWnBtKWgfYAFgJPFI2WCXfU0l7ALsB60+es/2FKvcOMsWghk3mbjWEJvcgWAupz9NgO6AYHbuc6S/+n25je7Wkh4HNSYr3SOC3wDOAd9kuTV1XJRHNB4CDScp2AXAYcBUw9Mo2CIJhRJ2YEbaQVEwXe05eZHXLAaRgqWeRUgn8WNIPJlfJzaiysj0K+BPg57b/QtLWwJdqEDYIgmBmTFRu+YDt+S2u/QbYoXC8fT7XrM3ybDLYhJSU63hSQYJVwH2SrgbmAy2VbZVw3SdsTwCrJW0M3Ncg4MBiOwyxQTCK1OONsBDYRdJOkuaSYggubWhzKfCmvH8UcIVtA78GXg4gaUPghUCpI3iVle0iSc8EPkPyUHgMuKbCfZVpkkD8tWVv9Vr08Szgk7aPmokMXrWa1b+7t7zRvffPpOtZoa0Teq+c1KskFnX7YIO2AQvAnA3WL70+8fjj7WWpwCAFLbRj4tFH+y1C76kpeXi2wZ4MXAaMAefZXiLpNGCR7UtJuby/KGkpsIKkkCF5MXwuJ8IS8DnbN5WNVyU3wmSS8LMlfQ/YuF2nM6Dr4APb95D+8gRBMOLU5I2A7QU0xA00RK8+SXLzarzvsWbnyygLati3cQM2A9bJ+z0jJwX/SM6dsCjLc5mkOyT9VW4zT9LNef8ESd+Q9D1Jv5T0sV7KGwTBLDNiuRE+UXLNZHtFTUwLPsj1xE7MK1aAX9veW9LpwPnAi0muaDcDZzfpc29gH1KqxdslfapJEvQgCIaQula2vaQsqOGQHsoxzYzQpJ7YpOF6MbCR7UeBRyU9lW3KjVxu+2EASbcAz2aqT93UoAbaJ/QIgmBAGNHk4YNC7cnDbZ9je77t+euyXm2CBkEwi1Q1IQzY6jeqFwRBMHwMmCKtwsCubCUtyO5cQRAEU5CrbYNElXBdkcriPMf2aZJ2BLax/bO6hGgWfFC02RaSgmP7fNILssZrD5CTijdpc3hFQdpcr7OocDCFCom/2/nRat25bfvwqpWVRQoaUBs7aRWf67oYMEVahSor2/9HKiF+XD5+lOTQ21ckjTeU05nXcP2HktaXdIakA/sjZRAEdSODJqptg0QVm+0LbO8r6ecAth/KoW39pmUghKQNgAnbT0raHxiWyhJBEFRhRL0RVuUkuwaQtCWdpIHoMZJ+SHIP20PSYmBPUlLgRleyIAiGlRH1RvgkcDGwlaQPkUJi/3FWpapG0yoMtg/JNdKWkey4h9uOlW0QjBCD9vKrClVyI3xZ0nXAK0gJF15r+9ZZl6w9ZfkU9iX9gTgMuLFVBxHUEARDyigq2+x98AfgW8Vztn89m4LNBEknAicDOwPPB3YE7pV0mO03NrYvVmrYWJsN4Y8vCNZCBtCtqwpVzAjfYU3hx/WBnYDbgd1nUa4ZYftcSZcC59o+QtLPbB/Qb7mCIKiZgX1r1JoqZoQ9i8c549fbWjQfBA4CrpK0A/CrfgsTBEH9jOrKdgq2r5fUWBStJe0Sg2fvgcOAfwEutF0pMXmzQAhJP7H9osKpjvJNBsNNpYCFdo750Fvn/GEiPpeuqGKz/bvC4RzSy6d7WjRvRs/8YRsUbRAEo8oQ6v0qfrZ/VNjWI9lwj+x24E78YSVdKen0nDj8Vkn75+Tgv5T0wUK7x/L/B+d7LpJ0m6Qv57DjIAiGnYp5EQbN1FC6ss3BDH9k+91djDEjf1hJ5wJn254sQ7zS9nxJfwt8E9iPVBPoDkmn236wYdx9SC/x7gGuJiUbv6qLeQRBMCiM0gsySevkgmgv7nKMGfnD2j6xoW0xefgS27/Nci4jVfttVLY/s708t7kBmEeDsg0/2yAYPsTgrVqrULay/RlJGd6Q3am+Bjyddsn2N2Y6aKf+sJlZSR5O+NkGwfBR02+rpEOBfydV1z3X9r80XF8P+ALpm/SDwDGTL/gl7QV8GtiYpIf2zwUim1LFG2H9PMjLWeNva2DGyjb8YYMgmDE12WOzmfQs4FXActI7o0tt31Jo9hbgIds7SzoW+ChwjKR1gC8B/8v2jZI2B1aVjVembLfKngg3s0bJTlLH35VSf9gmNtsgCIJEPSvbA4CltpcBSLqA9PK/qGyPBE7N+xcBZ+aX7a8GbrJ9I0CTd0bTKFO2Y8BGTFWyk1SeajN/2Hz+osJhs7rsJxb2Dy7sXwlc2eLaRi3anFxV3iAIhoB6lO12TC0CuxxojCF4uk1+h/UwsDnwXMCSLgO2BC6w/bGywcqU7W9tn9ah8AOBpK2B04EXAg8BK4GP2b645T3rjDH2zM1K+/UO25Ren7jhltLrdTJn/fVLr7uCA7pXVggCaNdPTR5162yzdds2q3/7u+4HqvC53P1/yt21d/jQT7qXoyptPl/NrVKdYnX7cSpUyhgkOkgMvoWk4rfjc/K7mm5ZB3gJsD8pd8zlkq6zfXnZDa0YSr/UvMS/BPi87ePzuWcDR/RVsCAI6qGzXLUP2J7f4tpvSJ5Mk2yfzzVrszzbaTchvcNaDvzI9gOQaiaSHApaKtuyoIZXlM1ggHk5ySf37MkTtn9l+1N9lCkIghqpKahhIbCLpJ1y9ZljWeNiOsmlwJvy/lHAFU5fGy8D9pT0jKyEX8ZUW+80Wq5sba9oK+pgsjtwfb+FCIJgFqnBZpttsCeTFOcYcJ7tJZJOAxbZvhT4LPBFSUtJQVTH5nsfkvRvJIVtYIHt75SN13EimmFD0lkk28pK2/s3XFsT1DCn6Xu8IAgGkLqCGmwvABY0nDulsP8kLRJa2f4Syf2rElVyIwwbS0i2EwBsv51kEtmysaHtc2zPtz1/7pzyF05BEAwQQ1iDbBSV7RXA+pL+unAuYnGDYESoaq8dtJDekVO22Xj9WuBlku6U9DPg88Df91eyIAhqYwhXtiNps81Jao7ttxxBEMwOg7ZqrUJfle1Mgg9K+joC2K0xkURVvHqc8QfbOGCseGgmXc8KE0+2zHfRW2rK3r/6d/fW0k8d7PDh8mIhWrdKIEGFgJEqtPl8/dRTpddHllC21ak7+CC7aTT6yAVBMIoMobLtp822UvCBpHm52sL5kn6Rqy68UtLVuVLDAbndCZLOzPvnS/qkpJ9IWibpqJ7OLAiC2cMpXLfKNkj0U9m2DD6Q9Kwc/jbJzsAngF3zdjzJd/bdwD+06H/b3OZwUjHJIAhGhGH0RhiYF2RNgg+KtcjutL04t1sCXG7buXbZvBZdXmJ7Argl24abjRmVGoJgGBkwRVqFfq5sKwcfML0yQ7FqQ6s/GMV7mibVKQY1rMt6VeUOgqDPDOPKtp/KNoIPgiDonKo+tqFsE2XBB01stkEQBGsYQmXbV5ttm+CD1+Q2dwF7FO45QdLWkv6T7J8r6RqSf+7JhTY/lPRt0suxV1USqF0ibLX52+ThSsBcKfF3TX60o0QVH9p2yd1hgHylhwwxeJ4GVRi6cN2Cf+6PbD/H9n4khb19oc0GwETO2LM/EHXMgmCEkF1pGySGTtnSxj9X0g+BxcAe2VthT1LVzNc07S0IguFiSG22A+P61QGlycFtHyLpPcAy4AHgcNvv6ZVwQRDMPoPmaVCFYVzZTkHSWZJulLSwcHpf4EZgr/x/q3tPkrRI0qJVrKUx5kEwjMTKticsAd4weWD77ZK2ABZJOhE4mRRx9nxgR+BeSYfZfmNjR7nK5jkAG2uzAfvRBEHQinhB1hta+ufaPhd4Nako297AUtvPb6ZogyAYUiJ5eG+okBz8IOAqSTsAv+qTmEEQzCZhRugNZf65ti8qHDYt1BYEwfAi6lu1SjoU+HdSdd1zG/NhS1oP+AKwH/AgcEz2/Z+8viOphPmptj9eNtasrmwngw9ymsPrJF0j6XUNbX4oaX1JZ0g6sMvxftKVwHb5NjFevg0b7ebbSz/FYZKlAhNPPtl2Q2q/Bc2p8ry0+VlJGgPOAg4DdgOOk7RbQ7O3AA/Z3plU6OCjDdf/DfhuFZFnTdn2I/jA9ou6uT8IguGgJpvtAaT3OstsrwQuAI5saHMkyUwJcBHwiqzbkPRa4E7SS/u2zObKtrbgA0lXSjo9u2ndKml/Sd/IycM/WGj3WP7/4HzPRTnx+JcnP6AgCIYcg8arbW3YDri7cLw8n2vaxvZq4GFgc0kbkd4T/VNVsWfTZttV8IGkc4GzbU+udlfani/pb4FvkmwoK4A7JJ1u+8GGIfbJMtwDXA28GLiqnqkFQdBXqluVtpBU/MZ8Tnb57JZTgdNtP1Z1HdezF2RNkoNDCj64mGQzmRJ8YPvEhi4m64stBpbkl2RIWgbsQDJeF/mZ7eW5zQ2kJONTlG0kDw+C4aSDF2QP2J7f4tpvSLpjku3zuWZtlktaB9iEpHpLICcAABvySURBVGteABwl6WPAM4EJSU/aPrOVILOpbGsLPsgUE4Y3JhNvNo9im/FmbSKoIQiGEFPXC9OFwC6SdiIp1WNJJbeKXAq8CbgGOIrkw2/gpZMNJJ0KPFamaGF2bbYRfBAEwaxQxwuybIM9GbgMuBX4qu0lkk6TNFnl+7MkG+1S4O+A981U5llb2eYaYa8FTpf0XuB+4HEqBh80sdkGQRAkavoeansBsKDh3CmF/Sdp469v+9QqY82qzbab4IOizdb2wYX9K4ErW1zbqEWbkzuVPQiCwUQ2mhg+q99QRpBVJa+sLwaeb/u2tjfMGSu/7jbZL3rpeN+trFCPvHVVexikqhHtZKlLjgr9jG2+Wen18Ycebj/OMAbctGHQ8h5UYehyI3TIcSQPhOP6LUgQBDUyhLkRRlbZZqfjl5DC7VrVOQuCYAiJrF+DxZHA92z/AnhQ0n79FigIghowMOFq2wAxysr2OFKsM/n/aaaEqNQQBMOJJqptg8RIviCTtBkpN8OekkxKn2ZJ78kOyUAENQTB0DJglXOrMKor26OAL9p+tu15tncgZed5aZv7giAYAsJmOzgcR3L5KvJ1wishCIafqp4IA6ZsR9KMYPuQJuc+WXaPxsYY22Tj8o7Hyv82jT/QmAtn9tCcNr6gY3Pb9uGnurdTa6yNvy/g8fZ+nmObbdq2zfiDKyrJ1C1z1luv9PrEk0/2RA6A8RUPlV6//60vbNvHlp/+afuBhuhrearUMDzyTjIQK1tJr5VkSbt20ccRkmYctxwEwRAxUXEbIAZC2VJD8IHtSxvrBwVBMIIYNOFK2yDRd2XbLvhA0rxcbeF8Sb/IVRdeKenqXKnhgNzuBEln5v3zJX1S0k9y/bOjejqpIAhmkYr1xwbM1NB3ZUuT4ANJz5JUzMSzM/AJYNe8HU9S0O8G/qFFv9vmNocDTVe8RT/ble6dHS4Igu4Ib4SZMS34wPY9tou1yO60vdj2BCkp+eXZX3YxqQJDMy6xPWH7FmDrZg1sn2N7vu35c7V+LZMJgqAHDOHKtq/eCFWDD5hemaFYtaHVHIr3RLHHIBgVPHjRYVXo98o2gg+CIOicIVzZ9lvZtgo++IsGm20QBMHTDKM3Ql/NCFWCD2zfBexROD4BpiYGz+fPB87PzdYDrpP0TuDeyQoOpbKMjzP+8COlbeZsMDh2Xa9eXd6gQiBB2wTk0DbxdFs5KjLx2OO19FMHEytX9VuENbRZnW159jVtuxh73s5t24zfvrSySAPBgK1aq9DvlW03lPnmzrN9J/Ay4Ec9lSoIgtnFRFBDr2jlm5t9cG8BdpV0A6mC73dy6fQgCEYA4VSHrMLWti/pUEm3S1raLAJV0nqSLszXr5U0L59/laTrJC3O/7+83VjDmhvhad9cSQ9K2s/2dbbfKOloYEfgIuDjtksrYwZBMITUYEaQNAacBbwKWA4slHRpdhed5C3AQ7Z3lnQs8FHgGOAB4M9s3yNpD1I59O3KxhvKlS3licH3BW4E9sr/tySShwfBEGJg3NW2cg4AltpeZnslSZcc2dDmSODzef8i4BWSZPvntu/J55cAG0gqzWA0dCvbEt/cHwIfAnYiRY1tCTwu6RXNXsRBJA8PgmGlpqxf2wF3F46XAy9o1cb2akkPA5uTVraTvAG43nbpim0YV7atfHMfBfYDbra9J+mvzT6tFG0QBENMdT/bLSa/vebtpDrFkLQ7ybTw1nZth25lSzIZfLTh3GRi8D8AN0qaC6xru9yXKwiCIaSjgIUHbM9vce03wA6F4+3zuWZtlktaB9gEeBBA0vYk99M/t31HO0GGTtlW8M1dlP8/qDcSBUHQU0xdfrYLgV0k7URSqseSklwVuRR4E3AN6Vv1FbYt6ZnAd4D32b66ymCzrmyLwQe2b2u49hVS1q4jScEHX+linAXA8bZ/P2Nh2zjwTzw+OI73banyMLpC4EOPqKNqRG20eQ6GjaELWKhCDT602QZ7MsmTYAw4z/YSSacBi2xfCnwW+KKkpcAK1riankzKRniKpFPyuVfbvq/VeL1Y2RaDDz7QcG2e7TslvYwk/IxpyBIWBMEIo4l6IhZsLwAWNJw7pbD/JDDNfdT2B4EPdjLWrL4gqyP4ICcC/w9JP82JwA+WdJ6kWyWdX2h3l6QtcrLxWyV9RtISSd+XtMFszjMIgh5iYMLVtgFitr0RpiUGB7D9RtIq94O5zQLbe9s+F0DSaZKOKPSzKXAg8C6SDeV0YHeS+9feTcbdBTjL9u7A70muGUEQjARRqaEZMwo+sH1KtpdM8q1CsvB7GxKJz2sy7p22b8j717VoE0ENQTCsDKGynTWbbZ3BB0xNFt6YSLzZHIptxoGmZoQIagiCIWXAFGkVZnNlG8EHQRDUT9hsp9EqMfhxwD6UBB80sdkGQRBknNzzqmwDxKyZEboJPmhwvTihsH8XTRKJ5/15efeBhjYf71D0IAgGmcmV7ZAxdBFkVZE0TnqhJpLd9mTbP2lzU3eD9tKO1E7WIbRpBYPN2MYbl14ff6SH0fFD+HyPrLIFnrC9N4CkPwU+QqrcEATBsBPKdmDZGHio30IEQVAHg+fWVYVRVrYb5Oi09YFtSW5oQRAMO6ZaQdMBY5SVbdGMcCDwBUl75OAI8vmTgJMA1ucZ/ZEyCILOGcKV7TAmD+8Y29cAW5ACKIrnz7E93/b8dSmtaBEEwcBQ0cd2wDwWRnll+zSSdiVFsD3Yb1mCIOgSQ4rWHy5GWdlO2mwhuX+9yR6gBK5BEMycAVu1VqGvynZGvrCt+5pPKk/xDgDbY511ABorv8WrV89EtCAYCdr50WqdCupkVU3CDKHNtt8r29p8YW0vYk1UWhAEo4o9lN4Ig/SCrKUvrKTHJP1rTgb+A0kHSLoyJxM/Irc5WNK38/6pOcH4ZJt39HAeQRDMMp6YqLQNEv1e2bb0hZV0w+SqF9iQVGjtPZIuJiUdfxWwG/B5UkLxRnYFDgH+CLhd0n/YrutLTBAEfSOCGmZCS1/YgqIFWAl8L+8vBp6yvUrSYlokBge+Y/sp4ClJ9wFbA8uLDcLPNgiGkCFNRDMwZoRWvrCZVYVghKcTiOdqDa3+YDQmEJ/WboqfrcLPNgiGBk9U29og6VBJt0taKul9Ta6vJ+nCfP1aSfMK196fz9+e3zmV0u+V7dOEL2wQBFWwjWt4QSZpDDiLZJJcDiyUdKntWwrN3gI8ZHtnSccCHwWOkbQbqYjt7sCzgB9Iem6Ze2m/V7YbSLoh220vpOALW/CRDYIgmIInXGlrwwHAUtvLbK8k1Uk8sqHNkaT3QgAXAa+QpHz+AttP2b4TWJr7a0lfV7ZlvrBFm63tjQr7pza02yj/fyVwZYs2exAEwehQTwTZdsDdhePlwAtatbG9WtLDwOb5/E8b7t2ubLCBMSP0m0f90AP/teqCXzWc3oJU+aEV7a7PXpvpf7R7JcuojTNIsgz3OM19fRr7eXabPtvyKA9d9gNftEXF5utLKvrfn5MLvfYe27G12IBF3VzvZZsYZ/hlGbVxqrbp1wYcCFxWOH4/8P6GNpcBB+b9dUh/ONTYttiu1dZvm20QBEG/WAjsImmnXHz2WKb77F8KvCnvH0Xy93c+f2z2VtgJ2AX4WdlgYUYIgmCtxMkGezJpVToGnGd7iaTTSCvyS4HPAl+UtBRYQVLI5HZfBW4BVgNvd5tEV6Fsy2ln26li++lVmxhn+GUZtXGqtukbthcACxrOFat7Pwkc3eLeDwEfqjqWsr0hCIIgmEXCZhsEQdADQtkGQRD0gFC2FZG0s6QXNzn/Ykl/3A+Z8vhbSmqWT2JokLRjv2WYRNKRkt5eOL42p+lcJumofsqW5XmJpLP6LUfQOfGCLCPplJLLJkWWvL/JtUeAM4A/a+hvjJRp7OnP2Pav87U32z6vjTzrAW8gZTUr9nFaDhf8AHAy6Q+mJK0GPmX7tLJ+W4x1qO3vtWnzoiayfEHSe21/LLc52vbXCvd82PY/tJsT8Fpg39zm67bf0EaW7UjO8UVZftTJfFrNCXgr+Y1zZj1gf1Kaz8+RQjYr0Y0ctr9QuL4PcDzpRc2dwDeqylDoo/SZK3veSu7ZAnjQTV78lD3/ayuxsl3D4002kxJR/D2wte3FjTflc/OK5yT9DXAv8F/Ad/L2bUnHSNqWpCQn217eQp5vkuKvVzfIBPAu4MXA/rY3s70p6Y/BiyW9q0GW10v6paSHJT0i6VFJj+RrL8i/FB8utP9ioyD53MeBl5AUz/7A/Hy5qJga/xgdWnFOKrR5TovPY1KWjwJXA/8IvCdv7+5kPm3mNNd2MYTzKtsPZkWxYeH+rj/XMjkkPVfSByTdBnwK+DXphfYhtj/V0EeZLFWfubLnDUkvVErG/w1J+0i6GbgZuFfSlJ9zq+e/2fzXKvodxTGIGynh+D+SVhEfBbYCflnSfmnjMbB5k3Z/S1qVPEZKvPN/gF8CGzZpe3PJeD8Htmhyfkvg501keX6Lfk4HrgJ+n+f5RmBJk3a3kj1XmsnSbL/FcdM5Adc322/R9nZgvW7mUzanxp9lw7U76vxc28gxAfw3sHPh3LJWz1+JLJWeubLnLV9fBLyatLp+CHhhPr9ri2du2vO/tm+xsi0gaTNJHwRuIn392df239u+D1gk6S+b3HMicF3D6buBh5sMcYnt15Me9ncDNwIbAd+UdGVD259I2rOFqOvanha3bvt+YN2G0/favrVFP39v+yWkVdO3gU2BbST9VNKFhXY3A9u06MMt9psdt5rTn0yuyIC98v6UFVqBZUyfY6fzKZvTtS1+zm9laoRQHZ9rmRyvB34L/FDSZyS9gqnfAIqUyVL1mSt73gDWsf19JzPR72z/FMD2bU3atnr+12rCZpuR9K+kB/wcYE/bjzU0eSdwsaQ3ska5zgfmAq9raLsMuFLSd5iaxHy+UvLhHUmhfzcB99l+paRGBfIS4ARJd+Y+BNj2XqTKFa1ovLYo/4JfUpTF9jeA7ylVON6SlDDku8Cbbb9Q0vaFPrYAbpH0s4Y+jiAryizfBgXlKFK5oypz6qQS8h+AG/JX4aIs7+hgPi3nBJwIXCLpeOD6fG4/ku32tYV2dXyuLeXIn+0lkjYkfb1/J7CVpP8ALrb9/YqyfKTiM1f2vEFaaU/yRMMcGv+oNn3+bf8bazER1JCRNFkBYjVTH57Jh27j3O4QYDJl4xLbVzTp6wPNxrD9T/n6EuAU0i/x20ilfm60XbSrNc2OZPtX+Rf58SaXBaxv++lfIkmfa96N35yvb0D6BTw7y3M4cAXJVvnx3KZpxWPb/93sfCvK5tRBH29qdt725/P1tvPJ7UrnJOnlpMTQ0OTnXMfnWkWOhjE3JX2NP8b2K6rKktuUPnPtfjaFZ07ABqQ/etD8mSt9/tdWQtn2AUlfsv0/8/7PSb8A+9i+TtLGth+RtFmze22vmAV5rrD98oI8rwYOsv31mvrv6Zxmez7DJkcev+kzR3oX0dPnbW0llG2NSDrD9jslfYtmGWfh70heDVcX7jmM5D72O9t3SPq27cPz1zkz1U5n26Vv6wv9vtf2xyR9qpks+Wt3s/veYvuzef8q2y/JttSWq/0KsnQ9J0lftf0/lIp8NpvPXk1umzKfOubUxec6xfWqjs+2C1leQ3rmjgOeXcfzlvstff6zaWStJZRtjUjaL69Om341JLkpvd8NLmT5xcSHbf9Z89tmJMuf2f5Wu6/dhfZb5vP31yVDnUja1vZv25hXdvQs+3JW+VwlHQlsb/usfM+1JI8WA++1XdlXt1tZGto3+ut+3faZdciS+y99/js1O40aoWx7iKSFtvdvcW2x7T0bzu3FdCfzjh3aS+SZFhxBsllPC47I9sIdGmS5ng6pa06SNm7oY4Wk6213EhxRy5ya9Hs1cKyzv65SPb2Xk7wAPle0t86mHLnv55JWsMeREl9fCLzb9rQ/WrP9vK3thDfCLCDpcOCfWRPlJNKq5t6S2zZo6OM8YC9gCWveBJsOo4ckzSf5VjZGXO3F1OCIO3P75wD/Ieldtk/P5/4ZOIH0lrkoy8s7lKXrOSm5YP0T8CRrvqqaFAzRSXBEV3Nq87k2C4xYAazI3gW1yVFBltuAHwOH216a27+rSR+1PG+5r6bPf1Wz08jiAXD2HbWN5NS9Fw3O6sBXgL9s0v5E4MKGc7fUJMvtwBHATqSH/9kkOx1UDI7IfcytQZau50TyF50mc77WaXDEjOfU5nOtFBhR12fbRpbXkqrG3g18BngFcOds/GwKfTV9/tf2LVa2s8PdpIicRhtNJ76610jazVNr2M+E+50yzjejZXBEgw/mzcAzgfu6lKWOOd3BGrejRsp8fu2pK6tu51T2uV4r6S9tf6Z4UtMDI+qQo1QW25dQzV+3rucNWj//azVhs50FJO1P+hr13zRx6lY1X92Xkeoc/Y7mTuZVZXkFyV7XGATwjaKNs8l9RfvnfFLs/M1Md7zvRJau55Rf8nwOuJbpQQ2dyNLVnNp8rluxJsBgWmCE7XsL/XT92ZbJ0qL9NH/dup633Ffp87+2Esp2FpD0fVIs+mIKkTfuwKlbqebR3zXpo3IAQO7nS6T49Sm2ONtvVsXgCCWH+E83kaXToIau56QUaXVVkz4+3/Km5v10Naeyz7XQpjQwog45qspSoY9anrfcV9fP/ygSynYWkHSz7T3atyzt4xrbB9Ygy+22n9dlHy29KDrsp+s5Sfq57X1qkKWrOdXxudYhR12y1PW85b66fv5HkbDZzg4LJL3aU+PXO+Xnkv4T+BYVvhqW8JMabHE/lvQR0tfMoiyduifVMafvSjqpSR+dRjp1O6c6Ptc65KhLlrqeN6jn+R85YmU7C+SooA1JD+0qZuD6ogrx7hX7uRX4Y5IT+0ztpD9sIUunrl9dzylHOjXro3KkU+6nqznV8bnWIUddstT1vOW+un7+R5FQtiNOWcRVr2UZJQbpcx0kWYLWRD7bASY7h7c8roLtXzXbZiDLvmXHHfTT9ZwkbVN23EE/M55TXZ9rt3LUKUsdP5ugNaFsZwlJ15cdV6TxxcmMXqRI+nbZcUX+us1xVeqY02fbHFelqznV9Ll2LUeNstTyvOXx63j+R4owI9SIpJ1pyOqVz7+YlE1/aZ/k2tb2b1sdDwPqQZKZThmkz7U4tqSXkJKVd2xvDWaPWNnWyxmk1HWNPEKqS1UZSXX+oqxWodx5pwpBDQX9OkXSewv7Rzdc+/D0O5pySeGervPBznROKpQ6d8pC9nSpc1KeiZ7I0YRtJP2rpLtIAQUdrSRrft6K/W4hqVU5n7ULD0DM8KhswMKSa4sr9nEMsC1T4/wvn4EsAk4lZXpaQSrSdz9wSgd9vAAYa5DlizOQpWXOgsbjkj5aFpbsUJau5kSq7LtD4fgGYHNS2ZnKP6c6PlvguaSsbbeRAj3+BvhVh33U8rzl+14IXElKXrMPKSrud6RQ5ENn+jMblS38bOvlmSXXNii5VmQb4CzguUp1pW4CdpS0oe1m0V6tqJTRqw3HAp8AnqNUQvwmYCYvxtRiv9lxK9xiv1O6nVPTUufAg2rI6DXLckDFjF5tqOt5AzgT+AdgE1IJoMNs/1TSrqQkTN/rsL/Rot/afpQ2OsjqVdLHs/P/PyflOD2cVGX1B8CVHchSudx5SR9z8/83AS8l5b19EPhp1fnk++tY2Y6TzDGPknLuPlI4fqQDWbqaEx1k9Jrtz5aKGb168bzlPm4o7N/a+Dx20tcobrGyrZdOsnq1omo11HZUzehVRidVYsvopAJvU9xZBd4yup1TJxm9ZlMOXD2jVxl1PW/QWQXetY7wRpgFVCGrV4U+2lbgbXN/pYxeFfqpVCV2mOhmTuogo9dsylHSZ9MKvBXu6+p5y31UrsC7NhLKdkBRSQXeivdXLndeoa+BqRJbF93OSRUyevVCjrro9nkL2hPKdsBo5qurhgq8fRMuyTKlWu0oMChzUkMF3j7K8XT1Xdtv77c8o0L42Q4e03x1bX83nzujLxIBkraUtOUgKKVukLRj47l+zKnor5uPrwX+b/bZParX8mQZ9pH0r8D/I/nq3toPOUaVULaDx9ZuKHUOkM/N66UgSpwq6QFSnatfSLpf0im9lKNmag2O6IL3ktIqTrIe6av7wcw8FLpjJD1X0gck3QZ8Cvg16RvvIa6xzHkQynYQqcNXty6Kvrqb2d6U5Iz/4hn4cw4KlSvwzjJNK/A6hSR34q/bLbeRKvkebvsltj9FcrMLaiaU7eCxSNJfNp6UdCJr3Ml6xf8i2e2eziFrexnwP4E/77EsdVFXcES3bFo8aHjrvyW94/Ukv9ofSvqMUj2zCK+dBeIF2YAhaWvgYmAlTXx1bf+uh7K0LG9Sdm2QaeOeZPcowbWkL5OCBpr56x5s+7heyFEYd9JX9zjSSvcLVPfVDSoQynZAqcNXtwYZavHVDaZTp7/uLMg2I1/doJxQtkFL6vTVDZpTl79uMPiEsg2CIOgB8YIsCIKgB4SyDYIg6AGhbIPKSBqXdIOkmyV9TdIzuujr/MlIKUnnStqtpO3Bkl40gzHukrRF1fMNbR7rcKxTJb27UxmDtYdQtkEnPGF77+zytRL4q+JFSTNK2Wn7RNu3lDQ5GOhY2QbBIBHKNpgpPwZ2zqvOH0u6FLhF0phSLayFkm7KfqOTob9nSrpd0g+ArSY7knSlpPl5/1BJ10u6UdLlOdfqXwHvyqvql+Y8DV/PYyxUKqiJpM0lfV/SEknnUsE5X9Ilkq7L95zUcO30fP5y5Rpukv5Y0vfyPT9WqkIQBG2J5OFBx+QV7GGsKXOyL7CH7TuzwnrY9v6S1gOulvR9Uk2q5wG7AVsDtwDnNfS7JaniwEG5r81sr5B0NvDYZI5XSf8JnG77qpxY5jLg+aR6XFfZPk3S/we8pcJ03pzH2ABYKOnrTmVuNgQW2X5XzgXxAVI1hXOAv7L9S0kvICVtefkMPsZgLSOUbdAJG0i6Ie//GPgs6ev9zwohva8G9ipkrtoE2AU4CPiK7XHgHknN/ElfCPxosi/bK1rI8UpgN60p2rqxpI3yGK/P935H0kMV5vQOSZNVNHbIsj5IqjpwYT7/JeAbeYwXAV8rjL1ehTGCIJRt0BFP2N67eCIrnWLgg4C/sX1ZQ7vX1CjHHOCFtp9sIktlJB1MUtwH2v6DpCtpXabHedzfN34GQVCFsNkGdXMZ8NfK9atyCr8NgR8Bx2Sb7rbAIU3u/SlwkKSd8r2b5fOPAn9UaPd9UtlucrtJ5fcj4Ph87jAakr00YRPgoaxodyWtrCeZQ6rJRe7zKtuPAHdKOjqPIUl/0maMIABC2Qb1cy7JHnu9pJuBT5O+QV0M/DJf+wJwTeONtu8HTiJ9Zb+RNV/jvwW8bvIFGfAOYH5+AXcLa7wi/omkrJeQzAm/biPr94B1JN0K/AtJ2U/yOHBAnsPLgdPy+TcCb8nyLSElbwmCtkS4bhAEQQ+IlW0QBEEPCGUbBEHQA0LZBkEQ9IBQtkEQBD0glG0QBEEPCGUbBEHQA0LZBkEQ9IBQtkEQBD3g/wdeNCKAu44HvwAAAABJRU5ErkJggg==\n","text/plain":["<Figure size 432x288 with 2 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":412},"id":"zM3ubGg2Gcof","executionInfo":{"status":"ok","timestamp":1614900865589,"user_tz":-60,"elapsed":431384,"user":{"displayName":"Vojtech Lanz","photoUrl":"","userId":"15791523486540207166"}},"outputId":"ea8160d5-4432-4682-9261-d18c101e737b"},"source":["#!/usr/bin/env python3\n","# hop length 22050, sample rate 44100, flattened, window size 5, normalized to C major, skip 1\n","# Prepare data\n","x, y = IsophonicsDataset.load_preprocessed_dataset(\"./PreprocessedDatasets/isophonics_5_true_500_1_true.ds\")\n","train_x, dev_x, train_y, dev_y = sklearn.model_selection.train_test_split(x, y, test_size=args.test_size, random_state=args.seed)\n","\n","# Create model\n","mlp_model = MLP_scalered(random_state=7)\n","\n","# Train model\n","mlp_model.fit(train_x, train_y)\n","\n","# Display score\n","print(\"\\n\",\"Dev accuracy: \", \"{:.2f}\".format(100*mlp_model.score(dev_x, dev_y)), \"%\", \"\\n\")\n","mlp_model.display_confusion_matrix(dev_x, dev_y)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["[INFO] The Preprocessed Isophonics Dataset was loaded successfully.\n","[INFO] The MLP model with scaler preprocessing was successfully created.\n","[INFO] The MLP model was successfully trained.\n","\n"," Dev accuracy:  50.68 % \n","\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 2 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"InPjy1YPS_Ay"},"source":["### Bass and Third double MLP model *(54.29%)*"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":478},"id":"gXmooNyKTCfR","executionInfo":{"status":"ok","timestamp":1615231297212,"user_tz":-60,"elapsed":608729,"user":{"displayName":"Vojtech Lanz","photoUrl":"","userId":"15791523486540207166"}},"outputId":"763b5c60-65a6-4661-f5f8-8779ccf9d249"},"source":["#!/usr/bin/env python3\n","\n","# Prepare data\n","x, y = IsophonicsDataset.load_preprocessed_dataset(\"./PreprocessedDatasets/isophonics_22050_11008_5_flattened_mel_log_1skip.ds\")\n","train_x, dev_x, train_y, dev_y = sklearn.model_selection.train_test_split(x, y, test_size=args.test_size, random_state=args.seed)\n","\n","# Create model\n","bass_third_model = BassVsThird(max_iter=500, random_state=7)\n","\n","# Train model\n","bass_third_model.fit(train_x, train_y)\n","\n","# Display score\n","print(\"\\n\",\"Dev accuracy: \", \"{:.2f}\".format(100*bass_third_model.score(dev_x, dev_y)), \"%\", \"\\n\")\n","bass_third_model.display_confusion_matrix(dev_x, dev_y)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["[INFO] The Preprocessed Isophonics Dataset was loaded successfully.\n","[INFO] The MLP model was successfully created.\n","[INFO] The MLP model was successfully trained.\n","[INFO] The accuracy of the bass model is  59.41 %\n","[INFO] The accuracy of the third model is  81.75 %\n","\n"," Dev accuracy:  54.29 % \n","\n","[INFO] The accuracy of the bass model is  59.41 %\n","[INFO] The accuracy of the third model is  81.75 %\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 2 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"swL8rxfAibTq"},"source":["### MLP -> RNN model *(47,37%)* \n","(**On 46s long sequences!!**)"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"7FX9Wl4XkJGF","executionInfo":{"status":"ok","timestamp":1615218730299,"user_tz":-60,"elapsed":120884,"user":{"displayName":"Vojtech Lanz","photoUrl":"","userId":"15791523486540207166"}},"outputId":"7d9cac72-b8ed-4d16-8796-99e4620af203"},"source":["#!/usr/bin/env python3\n","# Prepare data\n","x, y = IsophonicsDataset.load_preprocessed_dataset(\"./PreprocessedDatasets/Isophonics100_log_mel.ds\")\n","train_x, dev_x, train_y, dev_y = sklearn.model_selection.train_test_split(x, y, test_size=0.3, random_state=None)\n","\n","# Create model\n","model = MLP2RNN(input_shape=x.shape, output_classes=25, max_iter=500, random_state=7)\n"," \n","# Train model and display results\n","model.fit(train_x, train_y, dev_x, dev_y, epochs=35)\n"],"execution_count":null,"outputs":[{"output_type":"stream","text":["[INFO] The Preprocessed Isophonics Dataset was loaded successfully.\n","[INFO] The MLP model with scaler preprocessing was successfully created.\n","Model: \"sequential_2\"\n","_________________________________________________________________\n","Layer (type)                 Output Shape              Param #   \n","=================================================================\n","bidirectional_2 (Bidirection (None, 100, 128)          46080     \n","_________________________________________________________________\n","dense_4 (Dense)              (None, 100, 32)           4128      \n","_________________________________________________________________\n","dense_5 (Dense)              (None, 100, 25)           825       \n","=================================================================\n","Total params: 51,033\n","Trainable params: 51,033\n","Non-trainable params: 0\n","_________________________________________________________________\n","[INFO] The RNN model was successfully created.\n","[INFO] The acoustic model was successfully trained with dev accuracy 44.71 %\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAVsAAAEkCAYAAACBu5L5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nO29ebgdVZnv//kmYRJEgYAig8EGLyIgQ8AZAYcGLw0OcAXs23KVpm2lbfXiePuHSNtOrY1X4aqINKK2YKNi1Ag40YoTCQiEMEgMKHEAAyigDMk5398fax1SZ2fvqtrn1B7zfp6nnlNVa9Wqd9Wu8+6113oH2SYIgiDoLXMGLUAQBMGGQCjbIAiCPhDKNgiCoA+Esg2CIOgDoWyDIAj6QCjbIAiCPhDKNgiCDRZJh0m6WdIKSW9vU36QpKslrZV0dEvZzpIuk3SjpBskLSi717xmRR9d5m891wt22qi0zi03Pba03A8/3KRI5aiifMTMpzWv+lX02rV9kARQxcMdItt0zakeL3lysg+S1OM+7llte9vZtPGXh2zuu+6eqFX3quseutT2Ye3KJM0FzgJeCKwClkhaZPuGQrVfAScAp7Rp4nzgX2x/S9IWQOmDHktlK8nAv9n+3/n4FGAL26d1umbBThtx5aU7lbb73599VGn52lt/2bWsM6VKOXmyhkKYrPfClgtSpfWppZzmzt+uss7EHXfWkWjWaKONS8u9psaXakPPpaqdOZttVtnE5IMPVd+niXehBt/2RbP+J1l99wQ/vXTHWnU32v4X80uKDwRW2F4JIOkC4CjgEWVr+7ZcNk2RStoDmGf7W7ne/VWyjOs0wkPAyySVPeggCEYSM+HJWlsFOwC3F45X5XN1eDLwB0lflvQzSf+aR8odGVdluxY4G3jToAUJgqBZDEziWhswX9LSwnZSQ2LMA55Lml44AHgSabqh9IJx5SzgOkkf7FQhP/iTAHbeYZwfRRCMF5Pl06NFVtte2KHs10Bx7nDHfK4Oq4BrClMQFwPPAD7d6YJxHdli+17SBPYbSuqcbXuh7YXbblP6CyAIgiHBmAnX2ypYAuwmaRdJGwPHAotqirEEeKykqcW+QynM9bZjbJVt5iPAa4DNBy1IEATN0cU0QkdsrwVOBi4FbgS+aHu5pNMlHQkg6QBJq4BjgE9KWp6vnSBNIXxH0jKSfdCnyu431r+dbd8t6YskhXvuoOUJgmD2GFhTfxqhvC17MbC45dyphf0lpOmFdtd+C9i77r3GfWQL8GEgrBKCYEwwNDWN0FfGcmRrewtJjydNIxxA+olwoKQn2/55u2tuuWVrDn/RsaXtbn7+6tLy+w5qyLayDir/npyz+SbVojzwQHWdCkcCzSt3BAHwRLUN54NP27myzkaX9cfOds7W5c4r/bL3rYM227Syzpy51esRk/fd14Q4fWN43DTqM5bKVpKArwCfsX1sPvc04HFAW2UbBMFoYMzEqLlIMqbKFjgEWGP7E1MnbF87QHmCIGgKw8To6dqxVbZ7AlcNWoggCJrHiDWVwUGGj3FVtrUoOjVsutGWA5YmCII6GKgT+mPYGFdrhOXA/lWVik4NG88LU9wgGBUmUK1tmBhXZftdYJOiH7SkvSU9d4AyBUHQACaU7dBg28BLgRdI+kX2+ngf8LvBShYEQRNMWrW2YWJs52xt/wb4H7XrP/Agk9ffVFqnlh1tn6iKqVor5mof5KjLRt8anvXMRuxom7Knrmhn4q67m7nPCDE1sh01Bj6ylfR4SRfkEehVkhZLevIM2lko6aO9kDEIguHBiDWeW2sbJgY6sm3S+cD2UmBp40IGQTBUxMh2ZrR1PrD9g2IlSffnSOjLJX1b0oGSLpe0shCd52BJX8/7p0k6t1CnY5jFIAhGDTHhObW2YWLQ0nR0PpB0TeFwc+C7tp8K3Ae8h5Sk7aXA6R3a3h34S1KeoXdJqnbiD4Jg6EmZGubU2oaJoV0gs71P4fBh4JK8vwx4yPaaHEdyQYcmvmH7IeAhSXeSpiZWFStMc2rgUQ1KHwRBLxnFaYRBK9vlwNGVtdJUw9Sy7CQpoSO2JyV16kMxpegEbfpq+2xSrjK21NYj6JMSBBsetoZuiqAOg5Y4nA+CIOiKFDx8bq1tmBiosi1zPmiZsw2CIMiM5gLZoKcRypwP9inU2aKwf1rL9Vvkv5cDl3eos2dD4pYzZJHhR4p4dkFNphbImkDSYcD/BeYC59h+f0v5QaQkBHsDx9q+qKV8S1Kix4ttn1x2r+FS/V1Q5Qwh6Qs5a+YbJR03SFmDIGiWCavWVoakucBZwOHAHsBxkvZoqfYr4ATgPzo088/A9+vIPJLKtuAMcbntv7C9P/AOksXBFAts3wo8j5oPIwiC4ceICebU2io4EFhhe6Xth4ELgKOm3cu+zfZ1tMnEI2l/ks65rI7cA59GmCEdMzFI+jywL7B9nvfdDfiGpDNtnzMQaYMgaAwDa9yI6toBuL1wvAp4ep0LJc0hJZP9a+AFda4ZVWXb0RnC9islHQPsDFwEfMj2Mf0ULgiC3mGqpwgKzJdUdOM/O5t8zpbXAYttr0o/tKsZVWVbxX7Ad0iT2h1zj4VTQxCMJl0skK22vbBD2a+BnQrHO+ZzdXgm8FxJrwO2ADaWdL/tt3e6YFSVbVtnCEkvBt4L7AIcAWwL/EnS820f0lo/nBqCYPSwacqsawmwm6RdSEr2WOD4ejL4lVP7kk4AFpYpWhjRBTI6OEOQ4ibsD1xvey+SUt63naINgmBUEZM1tzJsrwVOBi4FbgS+aHu5pNMLAa4OkLQKOAb4ZPYFmBEjObK1bUkvBT4i6W3Ag8BtwBtJi2PXStoY2Mj2vYOTNAiCpjGNjWyxvRhY3HLu1ML+EtL0Qlkb5wHnVd2r58pW0uNJRsEHAH8A7gDeaPvnufwLwDtJJhd32P5CnXbbOUNIWgwcb/t1+dRBjXRi3U0bbS4Igu6ZCh4+avRU2dYMDr7A9q2Snkca0s8Y2y+ezfVBEIwGNWxoh45ej2wbsYeVdB7wQK6/HfBq4G9IK4I/tX1CrncbsJC0OvhN4ArgWaTJ76NsP9CrjgZB0B8MTA5Z3IM69FriUntY4F2kQOBHkWzW9plStMVJ6sxWJOX6JmARcAbwVGAvSfuwPrsBZ+WA438AXt5Ml4IgGCz10pgPW8zbQS+QdbSHLU5SZ76WF8aWkeZ2lwHk1cEFQGuUsFttT527ijZBxsPONghGj1Ed2fZa2TZiD5uZCgY+yfTA4JO070dr8PDNWiuEnW0QjCbDNmqtQ6+/HsIeNgiCRrHFmsl5tbZhoqfKtiw4OBX2sG3mbIMgCHI829k7NfSbnqv+kuDgAFMBItazh20xLD6hsH8baeGtXdmCvLu6pc6HupU7CIJhZTRzkA3XOLshJE2QsvBuBKwFzgfOsL1eTMrCRWijjcvbnVv+AU8++GC3os6YudtsXVo+cc8fqxuZnJi9IHNqGJeXPPYptHH5swfwQw9V1mkCzSv/t/DatX2Row7aZJPKOv16bv0iLZAN16i1DmOpbIEHplKhS9qOFGV9S5KpWRAEI84oOjWMnsRdYvtOknnXyaobeDIIgqHFiLWeW2sbJsZ1ZDsN2ytzvqHtSLEZgiAYUVKIxdEbN20QyrYT4dQQBKNJzNkOKZKeRHJsuLN4fppTw5xtwqkhCEYAo/AgG0YkbQt8Ajgz2/0GQTDijKIH2bgq281yJLEp06/PAv82WJGCIGiCMP0aIuzulyE1Zw5zNl8vfMJ05lY020c728n7/1RaPm+nJ1S2sfaXt1fW6RdzHlU9Zz7RL3tRDdFP1AoDmiqbYACvqWEX3ITNdd/Q0Fka1GGgb5WkCUnXSFou6VpJ/zvnY59pe0dKKk26FgTBaDNljVBnq0LSYZJulrSine6QdJCkqyWtlXR04fw+kn6cddd1kl5Rda9Bj2wbdT6wvYgU6zYIgjGmiQWybA56FvBCYBWwRNIi2zcUqv0KOAE4peXyPwN/Y/sWSU8ArpJ0qe0/dLrf0PxeKnM+kLRA0k2SzpP0c0mfl/QCST+UdIukA3O9EySdmffPk/RRST+StLL4rRQEweiSrBHqbRUcCKywvdL2w8AFpEQG6+5l32b7OlIo1+L5n9u+Je//hmTptG3ZzYZG2UJyPgDmAttJekJO4DjFrsCHgd3zdjzwHNI3zjs7NLl9rnME8P5eyR0EQX9pKOrXDkBx4WJVPtcVebC3MfCLsnqDnkboSP62KCZwvLUlO8N3CpkbFnRo5uIcfOYGSY9rLZzm1DBn8ybFD4KgR3RpjTBf0tLC8dnZvr4RJG1PsnZ6VWmgK4ZM2XZyPsi0ZmcoZm7o1I/iNet9OkWnhsfM2zZscINgFLBYO1nbGmG17YUdyn4N7FQ43jGfq4WkLYFvAP/H9k+q6g/NNEI4HwRBUIcGg4cvAXaTtEtOYnAsNRfYc/2vAOfbvqjONYNWtptNmX4B3wYuA94N0GbONgiCAKCRBTLba4GTgUuBG4Ev2l5ezBIj6QBJq4BjgE9mXQUpIcJBwAlZh13TIcv3IygGkYkttbWfPucFpXU0b6PScq95uEmRes4wBZ6uZZw/REG7R4o6kUX7pAe+7YuuKvlZX4vH7r6dn3dOp+Qv01n03LNmfb+mGPTItmvqOkJI+p6kTSV9RNIzByFrEAS9oSHTr74yVAtkNal0hJC0GTBp+0FJBwBvGYikQRA0TgoePnLjxNEb2RZp5wgh6Xuk/GN7ZrOwvUieIS/u3FIQBCODY2Q7EFqzMNg+RNJbgJWkLLtH2G47so3g4UEweoxq1K+RHtmWsB9wLbB3/tsW22fbXmh74UZULxYFQTAcxMh2ABQdISSdSDLl2BV4CrAzcIekw22/coBiBkHQEFOxEUaNkVa2bRwhzpG0CDjH9pGSrrR94GClDIKgaRzKti9UZWE4CLhC0k7ALwcgXxAEPcRmJK0ReqZsJU2QrAKmlOL5wBmtwRqy9cDhpKhcF9r+cVm7ZVkYJP3I9rMKp47pSugKw+5Rc1qool8OC3UIh4UeMoaOSzGynU7f7WFbFG0QBGPJaM7Z9mUsPlt7WEmXSzpD0lJJN2Z/5S/nwOHvKdS7P/89OF9zUQ46/vnWgORBEIwutmptw0Tf5my7tYeVdA7wCdtTsSgftr1Q0j8CXwX2B+4GfiHpDNt3tdxyX+CpwG+AHwLPBq7oYReDIOgDYWc7Mzraw9o+saBoYV3os2XActu/tf0QSVkXY1JOcaXtVXmO+BraBBiXdFIeLS9dw/DMXwZBUILTNHSdbZjo28i2AXvYYrDw1kDi7fpRrDPRrk4xePiW2nrIPpogCNphYCKsEdoT9rBBEDTHaC6Q9VLZzsoets2cbRAEATB8UwR16JmyLbOHzeXFVBLr2cPaPrGwf3Bh/3Lg8g5lW3Soc3KlwFJlMG2vqbAFnZyovE3f6FPA6DmPfnRlncn77qtuaIgCXI8SczbdtLLO5IMP9kGS/jJslgZ1GEUPsloUnCqmuMB2pDMPghEnLX6NnrIdvVnm+jxge5/CFoo2CMaEiUnV2qqQdJikmyWtkPT2NuUHSbpa0lpJR7eUvSrb+t8i6VVV9xrbkW0QBONLEyPbbPd/FvBCYBXJqWqR7RsK1X4FnACc0nLt1iRv2IUkA4mr8rX3dLrfOI9spzL3Tm2vGLRAQRDMHlPPe6yGQj4QWGF7pe2HgQuAo6bdy77N9nUkE9Mifwl8y/bdWcF+Czis7GbjPLJ9JDZDJyJTQxCMJl0slc6XVLRoOjvb1wPsANxeKFsFPL1mu+2u3aHsgnFWtpVMc2qYs00sdQfBKNDdAtnqSGUeBEEwU1xzK+fXTHf13zGfq0PX13Yc2Ur6GCXi2n5DTaEGxZRTxRSX2F5vtTEIgtFjsoalQQ2WALtJ2oWkKI8Fjq957aXAeyVtlY9fBLyj7IKyaYSR9tyqcqpoy2T5V6E2Kp918UPD5NRQ40eLZy/v5J/+XC1KhbNIaqh6GNK34O1zKl6dIXJe8UTrus34Y5qxRrC9VtLJJMU5FzjX9nJJpwNLbS/Kcba/AmwF/JWkd9t+qu27Jf0zSWEDnG777rL7ddQetj9TPJb0KNvV/1ld0qTzgaTFwPG2/9CIcEEQDB8GGnJqsL0YWNxy7tTC/hLSFEG7a88Fzq17r8oFMknPBD4NbAHsLOlpwN/Zfl3dm1RQaTVQF9vrBR4PgmD8GEXP7ToLZB8h2ZTdBWD7WlIQmb4h6TxJH5f0E0krcyaGc3PWhvMK9W6TNF/Sglz2KUnLJV2WU/AEQTAONLNA1ldqWSPYvr3lVJOTVm2dDySdLunIQr2tgGcCbyIFEj+DlIlhL0ntRsa7AWfZfirwB+DlrRWmBQ/3+AXrCILxRHiy3jZM1LGzvV3SswBL2gj4R+DGBmVoO41QnDfJfM22c76yO2wvA5C0nJSF4ZqW+rfanjp3FW0yNYSdbRCMIGMciOa1wOtJ3hG/AfbJx/2m8UwNQRCMKCM4jVCpgGyvBjqlqgmCIBgAYziylfQkSV+T9HtJd0r6as4n1hStc7bvz/dtnbMNgiBIjOPIFvgPUhiyl+bjY4EvUD9gQymdnA9abN1OKOzfBuzZoWxB3l0N7NlqwyvpvHz9emjeXOZus1W7okd46CmlcSaY+72rS8ubRPPKPzptVm18UcchoQkD/soMF8Adr69+nR73sR/NWpY6zH3MlqXlE/d0jKK3jqYyT1S0o02rHUY0t3q2cOSyOQyZIq1DHWX7KNufLRx/TtJbeiVQwzRmwxsEwZBghs7SoA5lsRG2zrvfzBHMLyB9n7yCFo+LIAiCvjJmI9urSF2a+gr5u0KZqQi6MCQUg9HcavulpbWDIBgNRtD0qyw2wi79FKRHlE4jTAsePneLvgkVBMHs0JiNbB9B0p7AHsAjeZNtn98rofpF0anhMRtvN4IfXxBsgAyhpUEd6gSieRdwMEnZLgYOB64ARl7ZBkEwimgkpxHqeJAdDTwf+J3t/wU8DXhMT6UKgiAoY7LmNkTUmUZ4wPZkzpu+JXAn09NBDC22YyI2CMaRcZxGAJZKeizwKZKFwv3Aj5sUok0A8Zd0cj4oaeMJwEdtHz0TGbxmLWvvXF1aZ95d5cbs/fz8vbbcUcD33dcfQRrKWvD4j19ZWadfz7eW00IVTQVcrWhnsl+f8zDRYPDwflInNsJUkPBPSLoE2DLnUW+SWTsf2P4NacojCIIxpylrBEmHAf+XlBbnnNYsMZI2Ia1P7U+K6f0K27flCIjnAPuR9Oj5tt9Xdq+Oc7aS9mvdgK2BeXm/b+Sg4O/LsROWZnkulfQLSa/NdRZIuj7vnyDpy5IukXSLpA/2U94gCHpMA7ERJM0lhSI4nGQAcJykPVqqvQa4x/aupBjaH8jnjwE2sb0XSRH/naQFZfcrG9l+uKTMwKFlDXfJes4HOZ/YiXnECvAr2/tIOgM4D3g2yRTteuATbdrcB9iXFGrxZkkfaxMEPQiCEaShke2BwArbKwEkXQAcBdxQqHMUcFrevwg4U5JIOnBzSfOAzYCHgXvLblbm1HDIDDswE9abRmiTT2xR/rsM2ML2fcB9kh7Kc8qtfMf2HwEk3QA8EZimbKc5NfCo2fciCIL+0Myc7Q5M1wmrWD/A1iN1cjbePwLbkBTvUcBvgUcBb5pxdt0hpPHg4dMyNWjrEVzfDIINkO6cGuZLWlo4Pjv/38+WA0l65QmklF0/kPTtqVFyO0ZJ2QZBECTqK9vVthd2KPs1081Yd8zn2tVZlacMHkNaKDseuMT2GuBOST8EFgIdlW2thI+DQNLibM4VBEEwDbneVsESYDdJu0jamBSre1FLnUXAq/L+0cB3bRv4FXndStLmwDOAm8puVsddV6S0OE+yfbqknYHH2642jKxJO+eD4pxtISg4ts8jLZC1lq0mBxVvU+eIeoKUu5x47ZC5pIwRVXbDdagKqN7UfTZYqgKiN2VbXIcGbpXnYE8GLiWZfp1re7mk04GlthcBnwY+K2kFcDdJIUOyYvj3nHBWwL9XmcTWmUb4f6R50UOB04H7gC8BB3TduwapcoSQ9D2SScf7gQttN+qIEQTBYJBBDY17bC+mJT53S5aYB0lmXq3X3d/ufBl1lO3Tbe8n6Wf5JvfkIfeg6egIIWkzYNL2g5IOAEYls0QQBHUYQQ+yOnO2a7LxrwEkbcvQhXhYRx7RLiPlIFsG7AUskdRqShYEwagypgkfPwp8BdhO0r+QJon/qadS1aNtFgbbh+QcaStJ87hH2I6RbRCMEWMZPNz25yVdRQqzKNLc6I09l6yasngK+5G+IA4Hru3UQDg1BMGIMo7KNlsf/Bn4WvGc7V/1UrCZIOlE4GRgV+ApwM7AHZIOt/3K1vrh1BAEI0g9s66ho840wjdYl/hxU2AX4GbgqT2Ua0bYPkfSIlL0niMlXWn7wEHLFQRBwwztqlFn6kwj7FU8zhG/Xteh+jBwEHCFpJ2AXw5amCAImmdcR7bTsH21pNZgDR3plT1sO0cIST+y/azCqa7s4PpqlB00Tjgs9Jj4/5gVdeZs31w4nENafPpNh+rt6Js9bIuiDYJgXBlBvV/HzvbRhW0T0hzuUbO9cTf2sJIul3RGDhx+o6QDcnDwWyS9p1Dv/vz34HzNRZJukvT57HYcBMGoUzMuwrBNNZSObLMzw6NtnzKLe8zIHlbSOcAnbE+FR3vY9kJJ/wh8lRQd/W7gF5LOsH1Xy333JS3i/Qb4ISnY+BWz6EcQBMPCOC2QSZqXAzU8e5b3mJE9rO0TW+oWg4cvt/3bLOdKUgi0VmV7pe1Vuc41wAJalG3Y2QbB6CGGb9Rah7KR7ZUkZXhNNqf6T+BPU4W2vzzTm3ZrD5uJ4OFBECRG8L+1jjXCpqRR46Gss7c1MGNlG/awQRDMmCGcj61DmbLdLlsiXM86JTtFE10ttYdtM2cbBEGQGDNlOxfYgulKdoraXW1nD5vPX1Q4bBcv8sTC/sGF/cuByzuUbdGhzsl15Q2CYAQYM2X7W9un902SBpH0OFKO92cA95DSDH/Q9lc6XjNvHnPnb1fa7pon71BaPueKa0rLgcYMw+dsumlFhWqrvskHH6qsw+RExX3mVjahOdVWd3N22bmyzsQtHdM7NYoO2Ku03EuWlZanRmpYGtZ5Fyqe75zNKt4DwA9Vf86j5hDSVPDwflL2HzmSdqnZnvZi4Pu2n2R7f1Iqix0HK1kQBI1QN5btkI1+y5Tt8/smRbMcSrLJ/cTUCdu/tP2xAcoUBEGDNOXUIOkwSTdLWiHp7W3KN5F0YS7/qaQFhbK9Jf1Y0nJJyySV/szoqGxt310t6lDyVODqQQsRBEEPaWBkm522ziLZ+e8BHCdpj5ZqrwHusb0raWryA/naecDngNfafipwMLCm7H5Dm8q8KSSdJelaSUvalJ2UXYCXPjz5wCDEC4JgBjQ0sj0QWGF7pe2HgQtYPxTBUcBn8v5FwPPzVOWLgOtsXwtg+y7bpQsc46hsl5OcMQCw/XrSlMi2rRVtn217oe2FG8/ZrI8iBkEwK5qZs90BuL1wvCqfa1vH9lrgj8A2wJMBS7pU0tWS3lp1s3FUtt8FNpX094Vz4YsbBGNC3VFtHtnOn/r1mreTGhJjHvAc4JX570slla5zdR3PdtixbUkvAc7I3za/J7kZv22wkgVB0Bj1LQ1W217YoezXpLgqU+yYz7WrsyrP0z6G5FG7imTxtBpA0mLSL+rvdBJk7JQtQA5Sc+yg5QiCoDc05K67BNhN0i4kpXoscHxLnUXAq4AfkzKLfzcP6C4F3irpUSQ7/ueRFtA6MlBlOxPng5K2jgT2sP3+mcjitWuZuOPO0jpz7vx9RSP9M+ybfPDBvt2rlCqnB8A1DNAnVtzagDDN4KXXN9BIQ+9CxfOd/NOfSsvHlgYeb45qeDJwKclj9lzbyyWdDiy1vQj4NPBZSStIIV2PzdfeI+nfSArbwGLb3yi738CUbcH54DO2j8/nnggcOZP28oNZVFkxCILRp6HvMtuLgcUt504t7D9Ih/Ratj9HMv+qxSAXyGo5H0hakLMtnCfp5znrwgsk/TBnajgw1ztB0pl5/zxJH5X0I0krJR3d154FQdA7nNx162zDxCCVbUfnA0lPyBPOU+wKfBjYPW/Hk1YATwHe2aH97XOdI0jJJIMgGBPGLi1OP5F0Fkk5Pmz7AKCYi+xW28tyveXAd/Ik9TJSBoZ2XGx7Erghzw23u2dkagiCUWTIFGkdBjmyre18wPqZGYpZGzp9YRSvaRtUp+jUsBGb1JU7CIIBM4oj20Eq23A+CIKge8Yw6ldPsW3gJcDzJN0q6UqSD/Lb2szZBkEQrGMEle1A52wrnA9enOvcBuxZuOaEwv4jZbbPA85rrZOP22aLWI+qgM+q+G4qj0MRlFH1bKF/z7cJG9mmgocH6yGGz9KgDiMZG0HS4yT9RzbruirHlHxpS53vSdpU0kckPXNQsgZB0Dyya23DxMgp2zqZGCRtBkxmg+QDgEgaGQTjQszZ9o1SZwhJ3wOWAXtm07C9gCWSXty2tSAIRo5RtEYYGjvbLijNxGD7EElvAVYCq4EjbL+lXd2wsw2CEWXIFGkdRnFkO40OmRj2A64F9s5/2xJ2tkEwmoyiu+4ojmyXAy+fOrD9eknzgaWSTgROJrn3PgXYGbhD0uG2XzkQaYMgaJYhnCKowyiObDs6Q9g+h5Qb6Lu29yHlF3pKKNogGDNigaz3lDlD5CoHAVdI2gn45YDEDIKgR4hYIFuPOsHBs/XA4aTIXBfa/nFVu52cIST9yPazCqfaxqEsabiiPJwWekaNIOQjxZDZeI4dI/h8ezayHYQ9bIuiDYJgTBnFkW0vpxEas4eVdLmkM3J2zBslHSDpyzl4+HsK9e7Pfw/O11yUA49/Piv/IAhGHYMm6m3DRC+VbaU9LPBJ4HUkC4JP2t4np6lA0jmSilkxH85ZMj8BfBV4PSkuwgmStmlzi32BNwJ7AE8Cnj37LgVBMBQ0tEAm6TBJN0taIentbco3kXRhLv+ppAUt5TtLul/SKVX36tsCWbf2sLZPtF2cVpjKL7YMWG77t7YfIjkvFNMRT8bwhzwAAByESURBVHGl7VU5gPg1tAkyLumkqXzya6aFvw2CYJhpYhpB0lzgLNKa0R7AcZL2aKn2GuAe27uS1p8+0FL+b8A368jcywWypu1hiwHDW4OJt+tHsc5Euzq2zwbOBthSWw/ZDE8QBG0xTS2QHUgyD10JIOkC4CjghkKdo4DT8v5FwJmSlDPFvAS4FaiV4riXI9uwhw2CoCc0tEC2A3B74XhVPte2ju21wB+BbSRtQTI3fXddmXumbGdrD9tmzjYIgiBRf852/tRUYd5OakiC04AzbN9f94Ke2tmWBQe3fVHhcD17WNsnFvYPLuxfDlzeoWyLDnVO7lb2IAiGE9losvY0wuq8sN6OXzN9vWfHfK5dnVWS5gGPAe4Cng4cLemDwGOBSUkP2j6zkyCjGBuhNnlO5SvAU2zfVOOCivKKHwL9NMxvwpJtmDISzJlbXWfcHB9qoHmz/xf12rUNSDJcNGRDuwTYTdIuJKV6LHB8S51FwKuAHwNHk6Y+DTz3EVmk04D7yxQtjKC7bpccB1yR/wZBMC40YPqV52BPBi4FbgS+aHu5pNMlHZmrfZo0R7sCeDOwnnlYXcZ2ZJsnsJ8DHAJ8DXjXYCUKgqApmvIOy3b9i1vOnVrYf5AKt3/bp9W51ziPbI8CLrH9c+AuSfsPWqAgCBrAwKTrbUPEOCvb44AL8v4FtJlKCKeGIBhNInj4kCBpa1Jshr0kGZgLWNJb8uQ2EE4NQTCyRNSvoeFo4LO2n2h7ge2dSJ4ez624LgiCESCifg0Px5FMvop8ibBKCILRp64lwpAp27GcRsgRxVrPfbTsGm00j3nztytveG65LejaX/+mhnTNoApZ6thnTj74YM/lAPBEtX3s3G3bBW6bzsQdd9aSabZoo41Ly73m4b7IAeCKRR4/Y8/KNvSjjjlPR5KUqWHINGkNhmJkK+klkixp91m0cWS7EGlBEIwhkzW3IWIolC0NOB/YXmT7/c2JFATBUGLQpGttw8TAlW3B+eA1tM8rtiBnWzhP0s9z1oUXSPphztRwYK53gqQz8/55kj4q6UeSVko6uq+dCoKghzhZI9TZhoiBK1vaOB9IeoKkolfHrsCHgd3zdjxJQZ8CvLNDu9vnOkeQkkmuR9HO9uHJB5rpTRAEPSesEWbGes4Htn9ju5iL7Fbby3LWheXAd7K97DLaZGDIXGx70vYNwOPaVbB9tu2FthduPGezRjoTBEEfGMGR7UCtEeo6H7B+ZoZi1oZOfSheE8keg2Bc8PB5h9Vh0CPbcD4IgqB7RnBkO2hl28n54H+1zNkGQRA8wihaIwx0GqGO84Ht20gpy6eOT4DpgcHz+fOA83K1TYCrJL0RuGMqg0OpLGsnmLjnD6V15jzqUVXN9I0qR4E6jgRNBP5uKjC1/zw8C5Reu2bQIqyjImC6fnJ9ZRNzt9qqss7EPffUFmkoGLJRax0GPbKdDWW2uQts3wo8D/h+X6UKgqC3mHBq6BedbHOzDe4NwO6SriFl8P1GTp0eBMEYIJzykNXYholRjY3wiG2upLsk7W/7KtuvlHQMsDMpx/uHbJdGWQ+CYAQZMkVah5Ec2VIeGHw/4Fpg7/y3I9OCh3v2QVmCIOgDBiZcb6tA0mGSbpa0ol1sFUmbSLowl/9U0oJ8/oWSrpK0LP89tOpeIzeyLbHN/R7wL8AuJK+xbYE/SXp+u4U4aAkePmeb0fuqDIINlCamCCTNBc4CXgisApZIWpQdoaZ4DXCP7V0lHQt8AHgFsBr4K9u/kbQnKWnkDmX3G8WRbSfb3PuA/YHrbe9F8jTbt5OiDYJghGnGzvZAYIXtlbYfJv1KPqqlzlHAZ/L+RcDzJcn2z2xPxVRdDmwmaZOym42isi0LDL4vcK2kjYGNbN/bb+GCIOg1jQWi2QG4vXC8ivVHp4/UyanP/wi0Bl9+OXC17dJEhiM3jVDDNndp/ntQfyQKgqCvmG4WyOZLWlo4PjtPHzaCpKeSphZeVFW358q26Hxg+6aWsi+QonYdRXI++MIs7rMYON52uWdCJ2z8UHmG3YmK8r4ygquxZUzed9+gRVjHKD3bCqcHGEGHhTrUt6FdbXthh7JfAzsVjnfM59rVWSVpHvAY4C4ASTuSdNvf2P5FlSD9mEboi/OB7RfPWNEGQTBSaHKy1lbBEmA3SbvkqcdjgUUtdRYBr8r7RwPftW1JjwW+Abzd9g/ryNxTZduE80EOBP5xST/JgcAPlnSupBslnVeod5uk+TnY+I2SPiVpuaTLJEX8xCAYFwxMut5W1kyagz2ZZElwI/BF28slnS7pyFzt08A2klYAbwamzMNOJsXZPlXSNXkrTWLY62mEGTkfSDodWGp76ltmK+CZwJGkb5pnAyeSTDX2sX1Ny313I8XF/VtJXyRNYH+ulx0NgqBfNBfRy/ZiYHHLuVML+w8C6zlG2X4P8J5u7tXraYQZOR/YPrWgaAG+VggWfkdLIPEFbe57a0EBX9WhznSnBoZoPjYIgnJGMMRiz0a2TTofMD1YeGsg8XZ9KNaZANpOI0xzatDWw/XJBEHQmSFTpHXo5cg2nA+CIGiehuZs+00vle2MnQ9aJqiDIAgKOJm81dmGiJ5NI8zG+aBlgvqEwv5ttAkknvcX5N3VLXU+1KXoQRAMM1Mj2xFj5DzI6iJpgrSgJtK87cm2f1R60Zy5s7vpkH2T9oUGsj001k5TsgwTdfpURUN91kYbl99mzcON3KcWo/Y5MsbKFnjA9j4Akv4SeB/JeSIIglEnlO3QsiUwhj6LQbAhMnxmXXUYZ2W7WfZO2xTYnmSGFgTBqGOgTkLTIWOclW1xGuGZwPmS9szOEeTzJwEnAWzK8GTODYKgghEc2Y5iPNuusf1jYD7JgaJ4/mzbC20v3IjSuL9BEAwNNW1sh8xiYZxHto8gaXeSB9tdg5YlCIJZYkje+qPFOCvbqTlbSOZfr7I9ehM9QRCsz5CNWuswUGU7I1vYzm0tJAXxfQOA7VkazQ45VfaXIzinNWvGsc9N2BY3RF/taKsYwc960CPbxmxhbS9lnVdaEATjij2S1gjDtEDW0RZW0v2S/jUHA/+2pAMlXZ6DiR+Z6xws6et5/7QcYHyqzhv62I8gCHqMJydrbcPEoEe2HW1hJV0zNeoFNielo3iLpK+Qgva+ENiDlGa4NZUFwO7AIcCjgZslfdz2mt51JQiC/hBODTOhoy1sQdECPAxckveXAQ/ZXiNpGR0CgwPfyKmFH5J0J/A4UqriRwg72yAYQUY0EM3QTCN0soXNrCk4IzwSQDxna+j0hdEaQHy9emFnGwQjiifrbRVIOkzSzZJWSHp7m/JNJF2Yy38qaUGh7B35/M15zamUQY9sHyFsYYMgqINt3MACmaS5wFmkKclVpJyGi2zfUKj2GuAe27tKOhb4APAKSXuQktg+FXgC8G1JTy4zLx30yHazqcyUwIUUbGELNrJBEATT8KRrbRUcCKywvdL2w6Q8iUe11DmKtC4EKTnt8yUpn7/A9kO2bwVW5PY6MtCRbZktbHHO1vYWhf3TWuptkf9eDlzeoc6eBEEwPjTjQbYDcHvheBXw9E51bK+V9Edgm3z+Jy3X7lB2s6GZRhg093HP6m9PXPjLltPzSZkfOlFV3rs6639p90uWKjlmdp/B9adOneG9z8ye/yCf7RMr6ldyH/dc+m1fNL9m9U0lFe3vz86JXvuP7dg6bMDS2ZT3s07cZ/RlGbf71K0zqA14JnBp4fgdwDta6lwKPDPvzyN9cai1brFep23Qc7ZBEASDYgmwm6RdcvLZY1nfZn8R8Kq8fzTJ3t/5/LHZWmEXYDfgyrKbxTRCEAQbJE5zsCeTRqVzgXNtL5d0OmlEvgj4NPBZSSuAu0kKmVzvi8ANwFrg9a4IdBXKtpyquZ06cz/9qhP3GX1Zxu0+desMDNuLgcUt54rZvR8Ejulw7b8A/1L3XsrzDUEQBEEPiTnbIAiCPhDKNgiCoA+Esq2JpF0lPbvN+WdL+otByBRseEh6jqSzBi1H0D2xQJaRdGpJsUmeJe9oU3Yv8BHgr1ram0uKNPbIM7b9q1z2atvnVsizCfByUlSzYhunt9TbNp//fVl7Ffc6zPYlFXWe1UaW8yW91fYHc51jbP9n4Zr32n5nVZ+A86aeTU15dyAZxxdl+X43/enUJ+CPwI62z8p1fsq64EhvtX1RF3LOWA7b5xfK9wWOJy3U3Ap8ua4MhTZK37m671vLNfOBu9xm4afs/d9QiZHtOv7UZjMpEMXbgMfZXtZ6UT63oHhO0j8AdwDfAr6Rt69LeoWk7YGTC3W/00Ger5L8r9e2yIQSp0laDdwM/FzS79t9YUh6maRbJP1R0r2S7pN0by57ev6neG+h/mfbtPFZ4EPAc4AD8rYwFx9bqNr6ZXRYzT5dXLjXlzo8j6nyDwA/BP4JeEveTummPxV9eivTbS03yWUHA39fuH7Wz7VMDklPlvQuSTcBHwN+RVrQPsT2x1raKJOl7jvX8X3L1zwjB+P/sqR9JV0PXA/cIWna59zp/W/X/w2KQXtxDONGCjj+T6RRxAeA7YBbSuqvaD0GtmlT7x9Jo5L7SYF3/g9wC7B5m7rXl9zvzaQXeZfCuSeR7AXf1EaWp3Ro5wzgCuAPuZ+vBJa3qXcj2XKlTdnP2u13OG7bp7I22tS9GdhkNv0p6xOwpOX4zML+T5p8rhVyTAL/BexaOLey0/tXIkutd67sfcvlS4EXkUbX9wDPyOd3b/M5t33/N/QtRrYFJG0t6T3AdaSfP/vZfpvtO4Glkv62zTUnAle1nL6d9HO0lYttv4z0sp8CXAtsAXxV0uUtdX8kaa8Oov5P4DinaEMA2F4J/DXwNy1177B9Y4d23mb7OaRR09eBrYDHS/qJpAsL9a4HHt+hDXfYb3fcqU9lbbSyEtioQ1nd/kDnPm01TTD75MJhMdZyE8+1TI6XAb8FvifpU5KeT3ITbUeZLHXfubL3DWCe7cucpol+Z/snALZvalO30/u/QRNzthlJ/0p6wc8G9rJ9f0uVNwJfkfRK1inXhcDGwEtb6q4ELpf0DaYHMV+oFHx4Z5Lr33XAnbZfIKlVgTwHOEHSrbkNAba9N7CR7fUCgNj+fZt2luZ/8IuLstj+MnCJUobjbUkBQ74JvNr2MyTtWGhjPnCDpCtb2jgSeFr+ySpSyMx7c7FI6Y4q+wQ8taQN296y0MafgWvyT+GiLG/ooj8d+wT8VNLf2v5UsbKkv2O6O2YTz7WjHPnZXixpc9LP+zcC20n6OPAV25fVlOV9Nd+5svcN0kh7igda+tD6Bdn2/bf9b2zAhFNDRtJUBoi1TH95pv3DSzoEmArZuNz2d9u09a5297D97ly+HDgV2B94HSnVz7XFUZSkttGRbP9S0tW29+vQj2llkv69fTN+dS7fjPQP+IkszxHAd4ErbH8o12mb8dj2f7U734myPnXRxqvanbf9mVxe2Z9cr1MW5xtZp7Suzuf2J83dvsT2Hfn6WT/XMjnaPVtJW5F+xr/C9vML50tlyXVK37mqzyZ/efyJ/IVI+tIjH29q+xHFXfX+b6iEsh0Akj5n+6/z/s9I/wD72r5K0pa275W0dbtrbd9dePHXa5qWF7+mPN+1fWhBnhcBB9kuXazqov3KPjVxn8L9Zt0fSYeSovBDhy/VfsjRFJ3eOdJaRN8+mw2ZULYNIukjtt8o6Wu0n3t8M8mq4YeFaw4nmY/9zvYvJH3d9hH555yZPk9n20+qKctbbX9Q0sfayZJ/dre77jW2P533r7D9HEn3UTLaryHLrPsk6Yu2/4dSks92/dm7zWXT+tNEn2bxXKeZXjXxbGchy4tJ79xxwBObeN9yu6Xvf54a2WAJZdsgkvbPo9NOP1HfQoqBuazlur2A99r+q/aXzUiWv7L9taqf3YX6s7bX7SWStrf924rplZ3dY1vOOs9V0lGsb6u7HUkBdWWrO1tZWuq32ut+yfaZTciS2y99/7uddho3Qtn2EUlLbB/QoWyZ7b1azu3N+kbmXRu0l8gj4F0kG8w5pFHNWuBjXt95YitgpxZZrqZLmuqTpC1b2ri7OF8t6Uu2X17RRiN9atPuD4Fjbd+ej68BDiVZAfx7cb61l3Lktp9MGsEeRwp8fSFwiu31vrR6/b5t6IQ1Qg+QdATwz6zzcppadb+j5LLNWto4F9gbWM66lWDTpfeQpIUk28pWj6u9gTcBzwYOcDYjk/Qk4OOS3mT7jHzun4ETSKvMRVkO7VKWWfcpWwW8G3iQdT9VTbIzLv4ELv35O9s+VTzXjacUbeaKPPd5d7YuaEyOGrLcBPwAOML2ilz/TW3aaOR9y221ff/rTjuNLR4CY99x20hG3XvTYqwOfAH42zb1TwQubDl3Q0Oy3AwcCexCevmfSJqnA/gZML/NNdsy3dHgZpICma0ss+4TyV50PZlz2dXt9kuey4z7VPFcV5Rc94sm5aghy0tIWWNvBz4FPB+4tRefTaGttu//hr7FyLY33E7yyGmdo+nGVvfHkvbw9Bz2M+H3ThHn21HXXvd64LHAnbOUpYk+/YJ1ZketlNn82tNHVrPtU9lzrWur24QcpbLYvph69rpNvW/Q+f3foIk52x4g6QDSz6j/oo1Rt+rZ6j6P5KP/O9obmdeV5fmk+bpWJ4Avq6a9bv6Z+lWSYmg1vO9Glln3KS/y/DvwU9Z3auhGlln1qeK5bkcNW90m5KiSpUP99ex1m3rfclul7/+GSijbHiDpMpIv+jIKnjfuwqhbKefRm9u0UdsBILfzOZL/+rS5ONuvrmuvq2QQ/8k2snTr1DDrPil5Wl3Rpo3PdLyofTuz6lPZcy3UqbTVbeLZ1pGlRhuNvG+5rVm//+NIKNseIOl623tW1yxt48e2n9mALDfb/m+zbKOjFUWX7cy6T5J+ZnvfBmSZVZ+aeK5NyNGULE29b7mtWb//40jM2faGxZJe5On+693yM0n/AXyNGj8NS/hRA3NxP5D0PtLPzKIs3ZonNdGnb0o6qU0b3Xo6zbZPTTzXJuRoSpam3jdo5v0fO2Jk2wOyV9DmpJd2DTMwfVENf/ea7dwI/AXJiH2m86Tf6yBLt6Zfs+5T9nRq10ZtT6fczqz61MRzbUKOpmRp6n3Lbc36/R9HQtmOOWUeV/2WZZwYpuc6TLIEnYl4tkNMNg7veFwH279st81Alv3KjrtoZ9Z9kvT4suMu2plxn5p6rrOVo0lZmvhsgs6Esu0Rkq4uO65J68LJjBZSJH297Lgmf19xXJcm+vTpiuO6zKpPDT3XWcvRoCyNvG/5/k28/2NFTCM0iKRdaYnqlc8/mxRNf8WA5Nre9m87HY8C6kOQmW4ZpudavLek55CClXc93xr0jhjZNstHSKHrWrmXlJeqNpKa/EdZqxzVC6BbhaCWhH7dIumthf1jWsreu/4VbamdFLKmTDPqk6SjJL0e0nOU9FNJKyWtJMWZ6IscbXi8pH+VdBvJoaCrkWTD71ux3fmSOqXz2bDwEPgMj8tGS7LAlrJlNdt4BbA90/38vzMDWQScRor0dDcpSd/vgVO7aOPpwNwWWT47A1k6xixoPS5po3ZSyF72iZTZd6fC8TXANqS0M7U/pyaeLfBkUtS2m0iOHv8A/LLLNhp53/J1zwAuJwWv2ZfkFfc7kivyYTP9zMZlCzvbZnlsSdlmJWVFHg+cBTxZKa/UdcDOkja33c7bqxO1InpVcCzwYeBJSinErwNmsjCmDvvtjjvhDvvdMts+tYvodRdwl1oievVYDqgZ0auCpt43gDOBdwKPIaUAOtz2TyTtTgrCdEmX7Y0Xg9b247TRRVSvkjaemP/+jBTj9AhSltVvA5d3IUutiF4VbWyc/14HPJcU9/Yu4Cd1+5Ovb2JkO0GajrmPFHP33sLxvV3IMqs+0UVEr14/W2pG9OrH+5bbuKawf2Pr+9hNW+O4xci2WbqJ6tWJutlQq+gmA28nuskSW0Y3GXjbYntuF/crY7Z96iaiVy/lwPUjepXR1PsG3WXg3eAIa4QeoBpRvWq0UZmBt+L62hl4K9qplSV2lJhNn9RFRK9eylHSZtsMvDWum9X7ltuonYF3QySU7ZCikgy8Na9vLAOvhihLbFPMtk9qIPtuE3I0xWzft6CaULZDRjtbXbVk4B2YcEmWadlqx4Fh6ZNaMvAOUI5Hsu/afv2g5RkXws52+FjPVtf2N/O5jwxEIkDStpK2HQalNBsk7dx6bhB9Ktrr5uOfAv9fttk9ut/yZBn2lfSvwP8j2ereOAg5xpVQtsPH49yS6hwgn1vQT0GUOE3SalKeq59L+r2kU/spR8M06hwxC95KCqs4xSakn+4HM3NX6K6R9GRJ75J0E/Ax4FekX7yHuME050Eo22GkCVvdpija6m5teyuSMf6zZ2DPOSzUzsDbY9pm4HVySe7GXne23ETK5HuE7efY/hjJzC5omFC2w8dSSX/belLSiawzJ+sX/5M0b/dIDFnbK4G/Bv6mz7I0RVPOEbNlq+JBy6r/tvSPl5Hsar8n6VNK+czCvbYHxALZkCHpccBXgIdpY6tr+3d9lKVjepOysmGmwjzJ7lOAa0mfJzkNtLPXPdj2cf2Qo3DfKVvd40gj3fOpb6sb1CCU7ZDShK1uAzI0YqsbrE+T9ro9kG1GtrpBOaFsg440aasbtKcpe91g+AllGwRB0AdigSwIgqAPhLINgiDoA6Fsg9pImpB0jaTrJf2npEfNoq3zpjylJJ0jaY+SugdLetYM7nGbpPl1z7fUub/Le50m6ZRuZQw2HELZBt3wgO19ssnXw8Bri4WSZhSy0/aJtm8oqXIw0LWyDYJhIpRtMFN+AOyaR50/kLQIuEHSXKVcWEskXZftRqdcf8+UdLOkbwPbTTUk6XJJC/P+YZKulnStpO/kWKuvBd6UR9XPzXEavpTvsUQpoSaStpF0maTlks6hhnG+pIslXZWvOaml7Ix8/jvKOdwk/YWkS/I1P1DKQhAElUTw8KBr8gj2cNalOdkP2NP2rVlh/dH2AZI2AX4o6TJSTqr/BuwBPA64ATi3pd1tSRkHDsptbW37bkmfAO6fivEq6T+AM2xfkQPLXAo8hZSP6wrbp0v678BranTn1fkemwFLJH3JKc3N5sBS22/KsSDeRcqmcDbwWtu3SHo6KWjLoTN4jMEGRijboBs2k3RN3v8B8GnSz/srCy69LwL2LkSuegywG3AQ8AXbE8BvJLWzJ30G8P2ptmzf3UGOFwB7aF3S1i0lbZHv8bJ87Tck3VOjT2+QNJVFY6cs612krAMX5vOfA76c7/Es4D8L996kxj2CIJRt0BUP2N6neCIrnaLjg4B/sH1pS70XNyjHHOAZth9sI0ttJB1MUtzPtP1nSZfTOU2P833/0PoMgqAOMWcbNM2lwN8r56/KIfw2B74PvCLP6W4PHNLm2p8AB0naJV+7dT5/H/DoQr3LSGm7yfWmlN/3gePzucNpCfbShscA92RFuztpZD3FHFJOLnKbV9i+F7hV0jH5HpL0tIp7BAEQyjZonnNI87FXS7oe+CTpF9RXgFty2fnAj1svtP174CTST/ZrWfcz/mvAS6cWyIA3AAvzAtwNrLOKeDdJWS8nTSf8qkLWS4B5km4E3k9S9lP8CTgw9+FQ4PR8/pXAa7J8y0nBW4KgknDXDYIg6AMxsg2CIOgDoWyDIAj6QCjbIAiCPhDKNgiCoA+Esg2CIOgDoWyDIAj6QCjbIAiCPhDKNgiCoA/8/6T3BrZTA3q2AAAAAElFTkSuQmCC\n","text/plain":["<Figure size 432x288 with 2 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["Epoch 1/35\n","14/14 [==============================] - 5s 145ms/step - loss: 3.1573 - accuracy: 0.1717 - val_loss: 2.8880 - val_accuracy: 0.3084\n","Epoch 2/35\n","14/14 [==============================] - 1s 80ms/step - loss: 2.7042 - accuracy: 0.2975 - val_loss: 2.2376 - val_accuracy: 0.3664\n","Epoch 3/35\n","14/14 [==============================] - 1s 80ms/step - loss: 2.2054 - accuracy: 0.3668 - val_loss: 2.0723 - val_accuracy: 0.3823\n","Epoch 4/35\n","14/14 [==============================] - 1s 79ms/step - loss: 2.1314 - accuracy: 0.3503 - val_loss: 1.9864 - val_accuracy: 0.3848\n","Epoch 5/35\n","14/14 [==============================] - 1s 77ms/step - loss: 1.9971 - accuracy: 0.3888 - val_loss: 1.9396 - val_accuracy: 0.4009\n","Epoch 6/35\n","14/14 [==============================] - 1s 78ms/step - loss: 1.9335 - accuracy: 0.4068 - val_loss: 1.9165 - val_accuracy: 0.4038\n","Epoch 7/35\n","14/14 [==============================] - 1s 78ms/step - loss: 1.9787 - accuracy: 0.3880 - val_loss: 1.8850 - val_accuracy: 0.4183\n","Epoch 8/35\n","14/14 [==============================] - 1s 77ms/step - loss: 1.8877 - accuracy: 0.4236 - val_loss: 1.8686 - val_accuracy: 0.4320\n","Epoch 9/35\n","14/14 [==============================] - 1s 78ms/step - loss: 1.9129 - accuracy: 0.4108 - val_loss: 1.8589 - val_accuracy: 0.4368\n","Epoch 10/35\n","14/14 [==============================] - 1s 80ms/step - loss: 1.8292 - accuracy: 0.4320 - val_loss: 1.8423 - val_accuracy: 0.4445\n","Epoch 11/35\n","14/14 [==============================] - 1s 77ms/step - loss: 1.8500 - accuracy: 0.4325 - val_loss: 1.8278 - val_accuracy: 0.4433\n","Epoch 12/35\n","14/14 [==============================] - 1s 79ms/step - loss: 1.8452 - accuracy: 0.4299 - val_loss: 1.8208 - val_accuracy: 0.4508\n","Epoch 13/35\n","14/14 [==============================] - 1s 77ms/step - loss: 1.8239 - accuracy: 0.4437 - val_loss: 1.8409 - val_accuracy: 0.4404\n","Epoch 14/35\n","14/14 [==============================] - 1s 76ms/step - loss: 1.8182 - accuracy: 0.4407 - val_loss: 1.8160 - val_accuracy: 0.4537\n","Epoch 15/35\n","14/14 [==============================] - 1s 78ms/step - loss: 1.8042 - accuracy: 0.4470 - val_loss: 1.8134 - val_accuracy: 0.4490\n","Epoch 16/35\n","14/14 [==============================] - 1s 76ms/step - loss: 1.7613 - accuracy: 0.4546 - val_loss: 1.7915 - val_accuracy: 0.4606\n","Epoch 17/35\n","14/14 [==============================] - 1s 81ms/step - loss: 1.7502 - accuracy: 0.4649 - val_loss: 1.7863 - val_accuracy: 0.4632\n","Epoch 18/35\n","14/14 [==============================] - 1s 79ms/step - loss: 1.7721 - accuracy: 0.4567 - val_loss: 1.7823 - val_accuracy: 0.4636\n","Epoch 19/35\n","14/14 [==============================] - 1s 79ms/step - loss: 1.7147 - accuracy: 0.4727 - val_loss: 1.7767 - val_accuracy: 0.4651\n","Epoch 20/35\n","14/14 [==============================] - 1s 77ms/step - loss: 1.7054 - accuracy: 0.4672 - val_loss: 1.7756 - val_accuracy: 0.4597\n","Epoch 21/35\n","14/14 [==============================] - 1s 79ms/step - loss: 1.7715 - accuracy: 0.4531 - val_loss: 1.7804 - val_accuracy: 0.4612\n","Epoch 22/35\n","14/14 [==============================] - 1s 79ms/step - loss: 1.7594 - accuracy: 0.4502 - val_loss: 1.7532 - val_accuracy: 0.4698\n","Epoch 23/35\n","14/14 [==============================] - 1s 79ms/step - loss: 1.6967 - accuracy: 0.4732 - val_loss: 1.7557 - val_accuracy: 0.4695\n","Epoch 24/35\n","14/14 [==============================] - 1s 78ms/step - loss: 1.7644 - accuracy: 0.4549 - val_loss: 1.7539 - val_accuracy: 0.4664\n","Epoch 25/35\n","14/14 [==============================] - 1s 77ms/step - loss: 1.7218 - accuracy: 0.4698 - val_loss: 1.7607 - val_accuracy: 0.4677\n","Epoch 26/35\n","14/14 [==============================] - 1s 78ms/step - loss: 1.7142 - accuracy: 0.4653 - val_loss: 1.7526 - val_accuracy: 0.4676\n","Epoch 27/35\n","14/14 [==============================] - 1s 76ms/step - loss: 1.6944 - accuracy: 0.4752 - val_loss: 1.7575 - val_accuracy: 0.4679\n","Epoch 28/35\n","14/14 [==============================] - 1s 79ms/step - loss: 1.6883 - accuracy: 0.4725 - val_loss: 1.7513 - val_accuracy: 0.4720\n","Epoch 29/35\n","14/14 [==============================] - 1s 80ms/step - loss: 1.7052 - accuracy: 0.4732 - val_loss: 1.7449 - val_accuracy: 0.4751\n","Epoch 30/35\n","14/14 [==============================] - 1s 80ms/step - loss: 1.7097 - accuracy: 0.4699 - val_loss: 1.7587 - val_accuracy: 0.4706\n","Epoch 31/35\n","14/14 [==============================] - 1s 79ms/step - loss: 1.6694 - accuracy: 0.4786 - val_loss: 1.7492 - val_accuracy: 0.4725\n","Epoch 32/35\n","14/14 [==============================] - 1s 79ms/step - loss: 1.6434 - accuracy: 0.4835 - val_loss: 1.7373 - val_accuracy: 0.4735\n","Epoch 33/35\n","14/14 [==============================] - 1s 77ms/step - loss: 1.6920 - accuracy: 0.4713 - val_loss: 1.7307 - val_accuracy: 0.4789\n","Epoch 34/35\n","14/14 [==============================] - 1s 78ms/step - loss: 1.6694 - accuracy: 0.4787 - val_loss: 1.7362 - val_accuracy: 0.4752\n","Epoch 35/35\n","14/14 [==============================] - 1s 80ms/step - loss: 1.6546 - accuracy: 0.4822 - val_loss: 1.7589 - val_accuracy: 0.4737\n","9/9 - 0s - loss: 1.7589 - accuracy: 0.4737\n","[INFO] The linguistic model was successfully trained with dev accuracy 47.37 %\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure 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\n","text/plain":["<Figure size 432x288 with 2 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"iOer3ByMMei-"},"source":["### MEL LOG *(39.66%)* vs. CQT *(28.70%)*spectrograms "]},{"cell_type":"code","metadata":{"id":"ymiC5II1xp41","colab":{"base_uri":"https://localhost:8080/","height":410},"executionInfo":{"status":"ok","timestamp":1615048536310,"user_tz":-60,"elapsed":3375208,"user":{"displayName":"Vojtech Lanz","photoUrl":"","userId":"15791523486540207166"}},"outputId":"427aeb93-9a31-4cdc-caca-890ad7b10523"},"source":["#!/usr/bin/env python3\n","\n","# MEL LOG spectrogram\n","\n","# hop length 22050, sample rate 44100, flattened, window size 5, normalized to C major, skip 10\n","# Prepare data\n","x, y = IsophonicsDataset.load_preprocessed_dataset(\"./drive/MyDrive/isophonics_22050_512_5_flattened_mel_log_10skip.ds\")\n","train_x, dev_x, train_y, dev_y = sklearn.model_selection.train_test_split(x, y, test_size=args.test_size, random_state=args.seed)\n","\n","# Create model\n","mlp_model = MLP_scalered(random_state=7)\n","\n","# Train model\n","mlp_model.fit(train_x, train_y)\n","\n","# Display score\n","print(\"\\n\",\"Dev accuracy: \", \"{:.2f}\".format(100*mlp_model.score(dev_x, dev_y)), \"%\", \"\\n\")\n","mlp_model.display_confusion_matrix(dev_x, dev_y)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["[INFO] The Preprocessed Isophonics Dataset was loaded successfully.\n","[INFO] The MLP model with scaler preprocessing was successfully created.\n","[INFO] The MLP model was successfully trained.\n","\n"," Dev accuracy:  39.66 % \n","\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 2 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":410},"id":"kN54JkBMZ2MC","executionInfo":{"status":"ok","timestamp":1615048660040,"user_tz":-60,"elapsed":3299594,"user":{"displayName":"Vojtech Lanz","photoUrl":"","userId":"15791523486540207166"}},"outputId":"8586c46b-0ded-441a-af27-fa0cdfaa902f"},"source":["#!/usr/bin/env python3\n","\n","# CQT spectrogram\n","\n","# hop length 22050, sample rate 44100, flattened, window size 5, normalized to C major, skip 1\n","# Prepare data\n","x, y = IsophonicsDataset.load_preprocessed_dataset(\"./drive/MyDrive/isophonics_22050_512_5_flattened_cqt_10skip.ds\")\n","train_x, dev_x, train_y, dev_y = sklearn.model_selection.train_test_split(x, y, test_size=args.test_size, random_state=args.seed)\n","\n","# Create model\n","mlp_model = MLP_scalered(random_state=7)\n","\n","# Train model\n","mlp_model.fit(train_x, train_y)\n","\n","# Display score\n","print(\"\\n\",\"Dev accuracy: \", \"{:.2f}\".format(100*mlp_model.score(dev_x, dev_y)), \"%\", \"\\n\")\n","mlp_model.display_confusion_matrix(dev_x, dev_y)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["[INFO] The Preprocessed Isophonics Dataset was loaded successfully.\n","[INFO] The MLP model with scaler preprocessing was successfully created.\n","[INFO] The MLP model was successfully trained.\n","\n"," Dev accuracy:  28.70 % \n","\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 2 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"LfujmiVa2GQo"},"source":["### Basic model, data transposed to C major key (and its modes .. dorian, phrygian, ...) - *43.12%*\n"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"k7dnE1sXgA67","executionInfo":{"status":"ok","timestamp":1614886003026,"user_tz":-60,"elapsed":1080882,"user":{"displayName":"Vojtech Lanz","photoUrl":"","userId":"15791523486540207166"}},"outputId":"2205fa57-c23c-4dfe-e4c9-040a5962d78e"},"source":["#!/usr/bin/env python3\n","\n","# Prepare data\n","x, y = IsophonicsDataset.load_preprocessed_dataset(\"./PreprocessedDatasets/isophonics_5_true_500_1_true.ds\")\n","train_x, dev_x, train_y, dev_y = sklearn.model_selection.train_test_split(x, y, test_size=args.test_size, random_state=args.seed)\n","\n","# Create model\n","mlp_model = MLP(random_state=7)\n","\n","# Train model\n","mlp_model.fit(train_x, train_y)\n","\n","# Display score\n","print(\"\\n\",\"Dev accuracy: \", \"{:.2f}\".format(100*mlp_model.score(dev_x, dev_y)), \"%\", \"\\n\")\n","mlp_model.display_confusion_matrix(dev_x, dev_y)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["[INFO] The Preprocessed Isophonics Dataset was loaded successfully.\n","[INFO] The MLP model was successfully created.\n","[INFO] The MLP model was successfully trained.\n","\n"," Dev accuracy:  43.12 % \n","\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 2 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"fX34Xs1gNLfF"},"source":["### Basic models, all data transposed to C key - *41.97%*"]},{"cell_type":"code","metadata":{"id":"-0Kh70L8zAO2","colab":{"base_uri":"https://localhost:8080/","height":412},"executionInfo":{"status":"ok","timestamp":1614170380894,"user_tz":-60,"elapsed":428729,"user":{"displayName":"Vojtech Lanz","photoUrl":"","userId":"15791523486540207166"}},"outputId":"c1d66f4c-1dd4-4a91-ce71-d7bc0120cdb8"},"source":["#!/usr/bin/env python3\n","\n","# Prepare data\n","x, y = IsophonicsDataset.load_preprocessed_dataset(\"./PreprocessedDatasets/isophonics_5_true_500_1_true-everything_to_C.ds\")\n","train_x, dev_x, train_y, dev_y = sklearn.model_selection.train_test_split(x, y, test_size=args.test_size, random_state=args.seed)\n","\n","# Create model\n","mlp_model = MLP()\n","\n","# Train model\n","mlp_model.fit(train_x, train_y)\n","\n","# Display score\n","print(\"\\n\",\"Dev accuracy: \", \"{:.2f}\".format(100*mlp_model.score(dev_x, dev_y)), \"%\", \"\\n\")\n","mlp_model.display_confusion_matrix(dev_x, dev_y)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["[INFO] The Dataset was loaded successfully.\n","[INFO] The MLP model was successfully created.\n","[INFO] The MLP model was successfully trained.\n","\n"," Dev accuracy:  41.97 % \n","\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 2 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"TeMBC0TF35iq"},"source":["### Basic MLP model - *27.31%*"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":410},"id":"Kj7gYW81In3d","executionInfo":{"elapsed":2382650,"status":"ok","timestamp":1613942756257,"user":{"displayName":"Vojtech Lanz","photoUrl":"","userId":"15791523486540207166"},"user_tz":-60},"outputId":"0b063b7e-d7c1-48cf-ee8b-8a22ddf69d62"},"source":["#!/usr/bin/env python3\n","\n","# Prepare data\n","x, y = IsophonicsDataset.load_preprocessed_dataset(\"./PreprocessedDatasets/isophonics_5_true_500_1.ds\")\n","train_x, dev_x, train_y, dev_y = sklearn.model_selection.train_test_split(x, y, test_size=args.test_size, random_state=args.seed)\n","\n","# Create model\n","mlp_model = MLP()\n","\n","# Train model\n","mlp_model.fit(train_x, train_y)\n","\n","# Display score\n","print(\"\\n\",\"Dev accuracy: \", \"{:.2f}\".format(100*mlp_model.score(dev_x, dev_y)), \"%\", \"\\n\")\n","mlp_model.display_confusion_matrix(dev_x, dev_y)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["[INFO] The Dataset was loaded successfully.\n","[INFO] The MLP model was successfully created.\n","[INFO] The MLP model was successfully trained.\n","\n"," Dev accuracy:  27.31 % \n","\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 2 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"t-Eb-u76vHgt"},"source":["# BILLBOARD DATASET"]},{"cell_type":"markdown","metadata":{"id":"-mYkFhTOvh-n"},"source":["## CNN, RNN model, chroma NNLS features - *75.89%*"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"k3cwBue15B8k","executionInfo":{"status":"ok","timestamp":1614966288212,"user_tz":-60,"elapsed":1566252,"user":{"displayName":"Vojtech Lanz","photoUrl":"","userId":"15791523486540207166"}},"outputId":"9602d9ce-d00f-4411-dc3b-de337eefce65"},"source":["#!/usr/bin/env python3\n","# Prepare data\n","x, y = IsophonicsDataset.load_preprocessed_dataset(\"./PreprocessedDatasets/Billboard500.ds\")\n","n_samples, n_frames, n_chromas = x.shape\n","x = x.reshape((n_samples,n_frames,n_chromas,1))\n","train_x, dev_x, train_y, dev_y = sklearn.model_selection.train_test_split(x, y, test_size=args.test_size, random_state=args.seed)\n","\n","# Create model\n","crnn_model = CRNN_1(input_shape=(n_frames, n_chromas, 1), output_classes=25)\n"," \n","# Train model\n","crnn_model.fit(train_x, train_y, dev_x, dev_y, epochs=85)\n","\n","# Display score\n","crnn_model.display_training_progress()\n","print(\"\\n\",\"Dev accuracy: \", \"{:.2f}\".format(100*crnn_model.score(dev_x, dev_y)), \"%\", \"\\n\")\n","crnn_model.display_confusion_matrix(dev_x, dev_y)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["[INFO] The Preprocessed Isophonics Dataset was loaded successfully.\n","Model: \"sequential\"\n","_________________________________________________________________\n","Layer (type)                 Output Shape              Param #   \n","=================================================================\n","conv2d (Conv2D)              (None, 500, 24, 16)       160       \n","_________________________________________________________________\n","batch_normalization (BatchNo (None, 500, 24, 16)       64        \n","_________________________________________________________________\n","conv2d_1 (Conv2D)            (None, 500, 24, 16)       2320      \n","_________________________________________________________________\n","batch_normalization_1 (Batch (None, 500, 24, 16)       64        \n","_________________________________________________________________\n","conv2d_2 (Conv2D)            (None, 500, 24, 16)       2320      \n","_________________________________________________________________\n","batch_normalization_2 (Batch (None, 500, 24, 16)       64        \n","_________________________________________________________________\n","max_pooling2d (MaxPooling2D) (None, 500, 8, 16)        0         \n","_________________________________________________________________\n","conv2d_3 (Conv2D)            (None, 500, 8, 32)        4640      \n","_________________________________________________________________\n","batch_normalization_3 (Batch (None, 500, 8, 32)        128       \n","_________________________________________________________________\n","conv2d_4 (Conv2D)            (None, 500, 8, 32)        9248      \n","_________________________________________________________________\n","batch_normalization_4 (Batch (None, 500, 8, 32)        128       \n","_________________________________________________________________\n","conv2d_5 (Conv2D)            (None, 500, 8, 32)        9248      \n","_________________________________________________________________\n","batch_normalization_5 (Batch (None, 500, 8, 32)        128       \n","_________________________________________________________________\n","max_pooling2d_1 (MaxPooling2 (None, 500, 3, 32)        0         \n","_________________________________________________________________\n","conv2d_6 (Conv2D)            (None, 500, 3, 64)        18496     \n","_________________________________________________________________\n","batch_normalization_6 (Batch (None, 500, 3, 64)        256       \n","_________________________________________________________________\n","conv2d_7 (Conv2D)            (None, 500, 3, 64)        36928     \n","_________________________________________________________________\n","batch_normalization_7 (Batch (None, 500, 3, 64)        256       \n","_________________________________________________________________\n","max_pooling2d_2 (MaxPooling2 (None, 500, 1, 64)        0         \n","_________________________________________________________________\n","conv2d_8 (Conv2D)            (None, 500, 1, 80)        46160     \n","_________________________________________________________________\n","batch_normalization_8 (Batch (None, 500, 1, 80)        320       \n","_________________________________________________________________\n","conv2d_9 (Conv2D)            (None, 500, 1, 80)        57680     \n","_________________________________________________________________\n","batch_normalization_9 (Batch (None, 500, 1, 80)        320       \n","_________________________________________________________________\n","reshape (Reshape)            (None, 500, 80)           0         \n","_________________________________________________________________\n","bidirectional (Bidirectional (None, 500, 192)          135936    \n","_________________________________________________________________\n","dense (Dense)                (None, 500, 25)           4825      \n","=================================================================\n","Total params: 329,689\n","Trainable params: 328,825\n","Non-trainable params: 864\n","_________________________________________________________________\n","[INFO] The CRNN model was successfully created.\n","Epoch 1/85\n","191/191 [==============================] - 55s 98ms/step - loss: 2.3382 - accuracy: 0.3191 - val_loss: 1.9795 - val_accuracy: 0.4085\n","Epoch 2/85\n","191/191 [==============================] - 17s 90ms/step - loss: 1.4630 - accuracy: 0.5601 - val_loss: 1.3723 - val_accuracy: 0.5845\n","Epoch 3/85\n","191/191 [==============================] - 17s 90ms/step - loss: 1.3169 - accuracy: 0.5993 - val_loss: 1.2421 - val_accuracy: 0.6203\n","Epoch 4/85\n","191/191 [==============================] - 17s 91ms/step - loss: 1.2126 - accuracy: 0.6264 - val_loss: 1.1835 - val_accuracy: 0.6393\n","Epoch 5/85\n","191/191 [==============================] - 18s 92ms/step - loss: 1.1482 - accuracy: 0.6424 - val_loss: 1.1901 - val_accuracy: 0.6343\n","Epoch 6/85\n","191/191 [==============================] - 18s 93ms/step - loss: 1.1113 - accuracy: 0.6517 - val_loss: 1.1085 - val_accuracy: 0.6570\n","Epoch 7/85\n","191/191 [==============================] - 18s 93ms/step - loss: 1.0848 - accuracy: 0.6583 - val_loss: 1.1475 - val_accuracy: 0.6430\n","Epoch 8/85\n","191/191 [==============================] - 18s 92ms/step - loss: 1.0394 - accuracy: 0.6715 - val_loss: 1.0471 - val_accuracy: 0.6714\n","Epoch 9/85\n","191/191 [==============================] - 18s 92ms/step - loss: 1.0070 - accuracy: 0.6794 - val_loss: 1.0706 - val_accuracy: 0.6642\n","Epoch 10/85\n","191/191 [==============================] - 17s 92ms/step - loss: 0.9756 - accuracy: 0.6876 - val_loss: 1.0119 - val_accuracy: 0.6823\n","Epoch 11/85\n","191/191 [==============================] - 17s 92ms/step - loss: 0.9279 - accuracy: 0.7034 - val_loss: 1.0478 - val_accuracy: 0.6689\n","Epoch 12/85\n","191/191 [==============================] - 18s 92ms/step - loss: 0.9448 - accuracy: 0.6949 - val_loss: 0.9775 - val_accuracy: 0.6901\n","Epoch 13/85\n","191/191 [==============================] - 18s 93ms/step - loss: 0.8790 - accuracy: 0.7172 - val_loss: 1.0115 - val_accuracy: 0.6789\n","Epoch 14/85\n","191/191 [==============================] - 18s 92ms/step - loss: 0.8672 - accuracy: 0.7197 - val_loss: 0.9727 - val_accuracy: 0.6936\n","Epoch 15/85\n","191/191 [==============================] - 18s 92ms/step - loss: 0.8637 - accuracy: 0.7186 - val_loss: 0.9552 - val_accuracy: 0.6951\n","Epoch 16/85\n","191/191 [==============================] - 18s 92ms/step - loss: 0.8306 - accuracy: 0.7266 - val_loss: 0.9460 - val_accuracy: 0.6991\n","Epoch 17/85\n","191/191 [==============================] - 18s 92ms/step - loss: 0.8129 - accuracy: 0.7350 - val_loss: 1.0043 - val_accuracy: 0.6869\n","Epoch 18/85\n","191/191 [==============================] - 17s 92ms/step - loss: 0.8037 - accuracy: 0.7364 - val_loss: 0.9268 - val_accuracy: 0.7041\n","Epoch 19/85\n","191/191 [==============================] - 18s 92ms/step - loss: 0.7891 - accuracy: 0.7431 - val_loss: 0.9176 - val_accuracy: 0.7088\n","Epoch 20/85\n","191/191 [==============================] - 18s 93ms/step - loss: 0.7596 - accuracy: 0.7486 - val_loss: 0.9149 - val_accuracy: 0.7067\n","Epoch 21/85\n","191/191 [==============================] - 18s 93ms/step - loss: 0.7500 - accuracy: 0.7529 - val_loss: 0.9091 - val_accuracy: 0.7105\n","Epoch 22/85\n","191/191 [==============================] - 18s 93ms/step - loss: 0.7386 - accuracy: 0.7558 - val_loss: 0.9039 - val_accuracy: 0.7125\n","Epoch 23/85\n","191/191 [==============================] - 18s 92ms/step - loss: 0.7171 - accuracy: 0.7627 - val_loss: 0.9113 - val_accuracy: 0.7120\n","Epoch 24/85\n","191/191 [==============================] - 18s 92ms/step - loss: 0.7082 - accuracy: 0.7651 - val_loss: 0.8845 - val_accuracy: 0.7204\n","Epoch 25/85\n","191/191 [==============================] - 18s 92ms/step - loss: 0.6700 - accuracy: 0.7783 - val_loss: 0.9034 - val_accuracy: 0.7127\n","Epoch 26/85\n","191/191 [==============================] - 18s 92ms/step - loss: 0.6693 - accuracy: 0.7794 - val_loss: 0.8809 - val_accuracy: 0.7216\n","Epoch 27/85\n","191/191 [==============================] - 18s 92ms/step - loss: 0.6806 - accuracy: 0.7745 - val_loss: 0.8753 - val_accuracy: 0.7222\n","Epoch 28/85\n","191/191 [==============================] - 18s 92ms/step - loss: 0.6493 - accuracy: 0.7852 - val_loss: 0.8665 - val_accuracy: 0.7274\n","Epoch 29/85\n","191/191 [==============================] - 17s 92ms/step - loss: 0.6397 - accuracy: 0.7876 - val_loss: 0.8583 - val_accuracy: 0.7323\n","Epoch 30/85\n","191/191 [==============================] - 18s 92ms/step - loss: 0.6112 - accuracy: 0.7960 - val_loss: 0.8748 - val_accuracy: 0.7217\n","Epoch 31/85\n","191/191 [==============================] - 18s 92ms/step - loss: 0.6148 - accuracy: 0.7932 - val_loss: 0.8702 - val_accuracy: 0.7249\n","Epoch 32/85\n","191/191 [==============================] - 17s 92ms/step - loss: 0.6106 - accuracy: 0.7956 - val_loss: 0.8557 - val_accuracy: 0.7303\n","Epoch 33/85\n","191/191 [==============================] - 18s 92ms/step - loss: 0.5917 - accuracy: 0.8025 - val_loss: 0.8681 - val_accuracy: 0.7264\n","Epoch 34/85\n","191/191 [==============================] - 18s 92ms/step - loss: 0.5872 - accuracy: 0.8031 - val_loss: 0.8603 - val_accuracy: 0.7303\n","Epoch 35/85\n","191/191 [==============================] - 18s 92ms/step - loss: 0.5657 - accuracy: 0.8103 - val_loss: 0.8430 - val_accuracy: 0.7348\n","Epoch 36/85\n","191/191 [==============================] - 18s 92ms/step - loss: 0.5637 - accuracy: 0.8100 - val_loss: 0.8530 - val_accuracy: 0.7345\n","Epoch 37/85\n","191/191 [==============================] - 18s 92ms/step - loss: 0.5622 - accuracy: 0.8105 - val_loss: 0.8436 - val_accuracy: 0.7385\n","Epoch 38/85\n","191/191 [==============================] - 18s 92ms/step - loss: 0.5291 - accuracy: 0.8210 - val_loss: 0.8424 - val_accuracy: 0.7362\n","Epoch 39/85\n","191/191 [==============================] - 17s 92ms/step - loss: 0.5342 - accuracy: 0.8200 - val_loss: 0.8953 - val_accuracy: 0.7251\n","Epoch 40/85\n","191/191 [==============================] - 18s 92ms/step - loss: 0.5437 - accuracy: 0.8160 - val_loss: 0.8351 - val_accuracy: 0.7429\n","Epoch 41/85\n","191/191 [==============================] - 18s 92ms/step - loss: 0.5078 - accuracy: 0.8289 - val_loss: 0.8538 - val_accuracy: 0.7381\n","Epoch 42/85\n","191/191 [==============================] - 17s 92ms/step - loss: 0.5137 - accuracy: 0.8261 - val_loss: 0.8454 - val_accuracy: 0.7403\n","Epoch 43/85\n","191/191 [==============================] - 18s 92ms/step - loss: 0.5051 - accuracy: 0.8284 - val_loss: 0.8371 - val_accuracy: 0.7424\n","Epoch 44/85\n","191/191 [==============================] - 18s 92ms/step - loss: 0.4911 - accuracy: 0.8346 - val_loss: 0.8351 - val_accuracy: 0.7456\n","Epoch 45/85\n","191/191 [==============================] - 18s 92ms/step - loss: 0.4943 - accuracy: 0.8318 - val_loss: 0.8348 - val_accuracy: 0.7457\n","Epoch 46/85\n","191/191 [==============================] - 18s 92ms/step - loss: 0.4820 - accuracy: 0.8369 - val_loss: 0.8571 - val_accuracy: 0.7395\n","Epoch 47/85\n","191/191 [==============================] - 18s 92ms/step - loss: 0.4769 - accuracy: 0.8388 - val_loss: 0.8372 - val_accuracy: 0.7470\n","Epoch 48/85\n","191/191 [==============================] - 18s 92ms/step - loss: 0.4586 - accuracy: 0.8445 - val_loss: 0.8420 - val_accuracy: 0.7432\n","Epoch 49/85\n","191/191 [==============================] - 18s 92ms/step - loss: 0.4519 - accuracy: 0.8462 - val_loss: 0.8350 - val_accuracy: 0.7470\n","Epoch 50/85\n","191/191 [==============================] - 17s 92ms/step - loss: 0.4405 - accuracy: 0.8502 - val_loss: 0.8411 - val_accuracy: 0.7475\n","Epoch 51/85\n","191/191 [==============================] - 18s 92ms/step - loss: 0.4391 - accuracy: 0.8504 - val_loss: 0.8710 - val_accuracy: 0.7372\n","Epoch 52/85\n","191/191 [==============================] - 18s 92ms/step - loss: 0.4578 - accuracy: 0.8439 - val_loss: 0.8544 - val_accuracy: 0.7460\n","Epoch 53/85\n","191/191 [==============================] - 18s 92ms/step - loss: 0.4465 - accuracy: 0.8468 - val_loss: 0.8476 - val_accuracy: 0.7468\n","Epoch 54/85\n","191/191 [==============================] - 18s 92ms/step - loss: 0.4277 - accuracy: 0.8539 - val_loss: 0.8429 - val_accuracy: 0.7485\n","Epoch 55/85\n","191/191 [==============================] - 18s 92ms/step - loss: 0.4229 - accuracy: 0.8551 - val_loss: 0.8379 - val_accuracy: 0.7524\n","Epoch 56/85\n","191/191 [==============================] - 18s 92ms/step - loss: 0.4144 - accuracy: 0.8575 - val_loss: 0.8507 - val_accuracy: 0.7485\n","Epoch 57/85\n","191/191 [==============================] - 18s 92ms/step - loss: 0.4140 - accuracy: 0.8588 - val_loss: 0.8402 - val_accuracy: 0.7529\n","Epoch 58/85\n","191/191 [==============================] - 18s 92ms/step - loss: 0.4082 - accuracy: 0.8601 - val_loss: 0.8609 - val_accuracy: 0.7473\n","Epoch 59/85\n","191/191 [==============================] - 18s 92ms/step - loss: 0.4201 - accuracy: 0.8553 - val_loss: 0.8462 - val_accuracy: 0.7490\n","Epoch 60/85\n","191/191 [==============================] - 18s 92ms/step - loss: 0.3997 - accuracy: 0.8630 - val_loss: 0.9166 - val_accuracy: 0.7324\n","Epoch 61/85\n","191/191 [==============================] - 18s 92ms/step - loss: 0.4283 - accuracy: 0.8513 - val_loss: 0.8540 - val_accuracy: 0.7531\n","Epoch 62/85\n","191/191 [==============================] - 18s 92ms/step - loss: 0.3776 - accuracy: 0.8704 - val_loss: 0.8525 - val_accuracy: 0.7520\n","Epoch 63/85\n","191/191 [==============================] - 18s 92ms/step - loss: 0.3788 - accuracy: 0.8697 - val_loss: 0.8520 - val_accuracy: 0.7535\n","Epoch 64/85\n","191/191 [==============================] - 18s 92ms/step - loss: 0.3843 - accuracy: 0.8669 - val_loss: 0.8618 - val_accuracy: 0.7516\n","Epoch 65/85\n","191/191 [==============================] - 18s 93ms/step - loss: 0.3908 - accuracy: 0.8654 - val_loss: 0.8514 - val_accuracy: 0.7529\n","Epoch 66/85\n","191/191 [==============================] - 18s 92ms/step - loss: 0.3579 - accuracy: 0.8774 - val_loss: 0.8602 - val_accuracy: 0.7514\n","Epoch 67/85\n","191/191 [==============================] - 18s 92ms/step - loss: 0.3529 - accuracy: 0.8774 - val_loss: 0.8589 - val_accuracy: 0.7530\n","Epoch 68/85\n","191/191 [==============================] - 18s 92ms/step - loss: 0.3659 - accuracy: 0.8743 - val_loss: 0.8784 - val_accuracy: 0.7475\n","Epoch 69/85\n","191/191 [==============================] - 18s 92ms/step - loss: 0.3839 - accuracy: 0.8665 - val_loss: 0.9092 - val_accuracy: 0.7423\n","Epoch 70/85\n","191/191 [==============================] - 18s 92ms/step - loss: 0.3608 - accuracy: 0.8748 - val_loss: 0.8592 - val_accuracy: 0.7558\n","Epoch 71/85\n","191/191 [==============================] - 18s 93ms/step - loss: 0.3520 - accuracy: 0.8776 - val_loss: 0.8535 - val_accuracy: 0.7570\n","Epoch 72/85\n","191/191 [==============================] - 18s 92ms/step - loss: 0.3427 - accuracy: 0.8819 - val_loss: 0.8684 - val_accuracy: 0.7547\n","Epoch 73/85\n","191/191 [==============================] - 18s 92ms/step - loss: 0.3414 - accuracy: 0.8812 - val_loss: 0.8662 - val_accuracy: 0.7577\n","Epoch 74/85\n","191/191 [==============================] - 18s 92ms/step - loss: 0.3385 - accuracy: 0.8822 - val_loss: 0.8775 - val_accuracy: 0.7530\n","Epoch 75/85\n","191/191 [==============================] - 18s 92ms/step - loss: 0.3441 - accuracy: 0.8801 - val_loss: 0.8734 - val_accuracy: 0.7577\n","Epoch 76/85\n","191/191 [==============================] - 18s 92ms/step - loss: 0.3381 - accuracy: 0.8823 - val_loss: 0.8793 - val_accuracy: 0.7554\n","Epoch 77/85\n","191/191 [==============================] - 18s 93ms/step - loss: 0.3263 - accuracy: 0.8856 - val_loss: 0.8722 - val_accuracy: 0.7559\n","Epoch 78/85\n","191/191 [==============================] - 18s 93ms/step - loss: 0.3188 - accuracy: 0.8890 - val_loss: 0.8970 - val_accuracy: 0.7494\n","Epoch 79/85\n","191/191 [==============================] - 18s 92ms/step - loss: 0.3280 - accuracy: 0.8855 - val_loss: 0.8874 - val_accuracy: 0.7544\n","Epoch 80/85\n","191/191 [==============================] - 18s 92ms/step - loss: 0.3396 - accuracy: 0.8815 - val_loss: 0.8801 - val_accuracy: 0.7583\n","Epoch 81/85\n","191/191 [==============================] - 18s 93ms/step - loss: 0.3210 - accuracy: 0.8869 - val_loss: 0.8846 - val_accuracy: 0.7602\n","Epoch 82/85\n","191/191 [==============================] - 18s 92ms/step - loss: 0.3133 - accuracy: 0.8914 - val_loss: 0.8940 - val_accuracy: 0.7592\n","Epoch 83/85\n","191/191 [==============================] - 18s 92ms/step - loss: 0.3853 - accuracy: 0.8665 - val_loss: 0.8833 - val_accuracy: 0.7559\n","Epoch 84/85\n","191/191 [==============================] - 18s 93ms/step - loss: 0.3154 - accuracy: 0.8896 - val_loss: 0.8855 - val_accuracy: 0.7549\n","Epoch 85/85\n","191/191 [==============================] - 18s 92ms/step - loss: 0.3067 - accuracy: 0.8933 - val_loss: 0.9023 - val_accuracy: 0.7589\n","[INFO] The CRNN model was successfully trained.\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["82/82 - 2s - loss: 0.9023 - accuracy: 0.7589\n","\n"," Dev accuracy:  75.89 % \n","\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 2 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]}]}