juliobguedes/log.txt

## log.txt
 > Preprocessing Spotify dataset. This process may take several hours.
 >> Checking if the Spotify MPD is already preprocessed.
 >> Preprocessed version was found. Skipping stage.
 > Loading Spotify MPD dataset.
 >> Dataset loaded.
 > Splitting dataset
 >> Sampling 1% of dataset
 >> Splitting sample with 20% for testing
 >> Creating Vocab.
 > Building tensorflow datasets.
 >> Building training and validation dataset. This process may take some time.
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 7241/7241 [00:00<00:00, 7809.39it/s]
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 7241/7241 [00:00<00:00, 8587.54it/s]
 >> Building testing dataset. This process may take a long time.
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1811/1811 [00:00<00:00, 8875.48it/s]
 Creating and compiling model
 > Retrieving candidates. This process takes more time as the number of unique items increases.
 Fitting model
Epoch 1/20
WARNING:tensorflow:The dtype of the source tensor must be floating (e.g. tf.float32) when calling GradientTape.gradient, got tf.int32
WARNING:tensorflow:Gradients do not exist for variables ['counter:0'] when minimizing the loss.
WARNING:tensorflow:The dtype of the source tensor must be floating (e.g. tf.float32) when calling GradientTape.gradient, got tf.int32
WARNING:tensorflow:Gradients do not exist for variables ['counter:0'] when minimizing the loss.
 1/57 [..............................] - ETA: 0s - top_k_categorical_accuracy: 0.0000e+00 - loss: 621.1177 - regularization_loss: 0.0000e+00 - total_loss: 621.1177WARNING:tensorflow:From C:\Users\lmd-pc-03\anaconda3\lib\site-packages\tensorflow\python\ops\summary_ops_v2.py:1277: stop (from tensorflow.python.eager.profiler) is deprecated and will be removed after 2020-07-01.
Instructions for updating:
use `tf.profiler.experimental.stop` instead.
 2/57 [>.............................] - ETA: 1:00 - top_k_categorical_accuracy: 0.0000e+00 - loss: 621.0904 - regularization_loss: 0.0000e+00 - total_loss: 621.0904WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.4531s vs `on_train_batch_end` time: 1.7454s). Check your callbacks.
57/57 [==============================] - 34s 596ms/step - top_k_categorical_accuracy: 0.0000e+00 - loss: 610.4406 - regularization_loss: 0.0000e+00 - total_loss: 610.4406 - val_top_k_categorical_accuracy: 2.7620e-04 - val_loss: 313.2148 - val_regularization_loss: 0.0000e+00 - val_total_loss: 313.2148
Epoch 2/20
57/57 [==============================] - 32s 554ms/step - top_k_categorical_accuracy: 0.0059 - loss: 607.5602 - regularization_loss: 0.0000e+00 - total_loss: 607.5602 - val_top_k_categorical_accuracy: 0.0000e+00 - val_loss: 312.8128 - val_regularization_loss: 0.0000e+00 - val_total_loss: 312.8128
Epoch 3/20
57/57 [==============================] - 32s 555ms/step - top_k_categorical_accuracy: 6.9051e-04 - loss: 592.5384 - regularization_loss: 0.0000e+00 - total_loss: 592.5384 - val_top_k_categorical_accuracy: 5.5241e-04 - val_loss: 313.7579 - val_regularization_loss: 0.0000e+00 - val_total_loss: 313.7579
Epoch 4/20
57/57 [==============================] - 32s 561ms/step - top_k_categorical_accuracy: 0.0019 - loss: 540.0459 - regularization_loss: 0.0000e+00 - total_loss: 540.0459 - val_top_k_categorical_accuracy: 9.6672e-04 - val_loss: 316.1017 - val_regularization_loss: 0.0000e+00 - val_total_loss: 316.1017
Epoch 5/20
57/57 [==============================] - 32s 569ms/step - top_k_categorical_accuracy: 0.0054 - loss: 459.0720 - regularization_loss: 0.0000e+00 - total_loss: 459.0720 - val_top_k_categorical_accuracy: 1.3810e-04 - val_loss: 323.2471 - val_regularization_loss: 0.0000e+00 - val_total_loss: 323.2471
Epoch 6/20
57/57 [==============================] - 32s 562ms/step - top_k_categorical_accuracy: 0.0134 - loss: 383.0080 - regularization_loss: 0.0000e+00 - total_loss: 383.0080 - val_top_k_categorical_accuracy: 5.5241e-04 - val_loss: 328.9191 - val_regularization_loss: 0.0000e+00 - val_total_loss: 328.9191
Epoch 7/20
57/57 [==============================] - 32s 559ms/step - top_k_categorical_accuracy: 0.0275 - loss: 318.0599 - regularization_loss: 0.0000e+00 - total_loss: 318.0599 - val_top_k_categorical_accuracy: 4.1431e-04 - val_loss: 340.4540 - val_regularization_loss: 0.0000e+00 - val_total_loss: 340.4540
Epoch 8/20
57/57 [==============================] - 32s 562ms/step - top_k_categorical_accuracy: 0.0441 - loss: 261.4458 - regularization_loss: 0.0000e+00 - total_loss: 261.4458 - val_top_k_categorical_accuracy: 2.7620e-04 - val_loss: 344.6627 - val_regularization_loss: 0.0000e+00 - val_total_loss: 344.6627
Epoch 9/20
57/57 [==============================] - 32s 559ms/step - top_k_categorical_accuracy: 0.0594 - loss: 212.6684 - regularization_loss: 0.0000e+00 - total_loss: 212.6684 - val_top_k_categorical_accuracy: 6.9051e-04 - val_loss: 352.9798 - val_regularization_loss: 0.0000e+00 - val_total_loss: 352.9798
Epoch 10/20
57/57 [==============================] - 32s 560ms/step - top_k_categorical_accuracy: 0.0853 - loss: 170.4088 - regularization_loss: 0.0000e+00 - total_loss: 170.4088 - val_top_k_categorical_accuracy: 6.9051e-04 - val_loss: 361.4565 - val_regularization_loss: 0.0000e+00 - val_total_loss: 361.4565
Epoch 11/20
57/57 [==============================] - 32s 561ms/step - top_k_categorical_accuracy: 0.1027 - loss: 135.3549 - regularization_loss: 0.0000e+00 - total_loss: 135.3549 - val_top_k_categorical_accuracy: 9.6672e-04 - val_loss: 371.1420 - val_regularization_loss: 0.0000e+00 - val_total_loss: 371.1420
Epoch 12/20
57/57 [==============================] - 33s 572ms/step - top_k_categorical_accuracy: 0.1159 - loss: 107.3723 - regularization_loss: 0.0000e+00 - total_loss: 107.3723 - val_top_k_categorical_accuracy: 0.0015 - val_loss: 376.0966 - val_regularization_loss: 0.0000e+00 - val_total_loss: 376.0966
Epoch 13/20
57/57 [==============================] - 33s 573ms/step - top_k_categorical_accuracy: 0.1334 - loss: 85.8168 - regularization_loss: 0.0000e+00 - total_loss: 85.8168 - val_top_k_categorical_accuracy: 0.0018 - val_loss: 383.9549 - val_regularization_loss: 0.0000e+00 - val_total_loss: 383.9549
Epoch 14/20
57/57 [==============================] - 34s 596ms/step - top_k_categorical_accuracy: 0.1280 - loss: 69.2909 - regularization_loss: 0.0000e+00 - total_loss: 69.2909 - val_top_k_categorical_accuracy: 0.0019 - val_loss: 384.4878 - val_regularization_loss: 0.0000e+00 - val_total_loss: 384.4878
Epoch 15/20
57/57 [==============================] - 32s 564ms/step - top_k_categorical_accuracy: 0.1480 - loss: 56.5529 - regularization_loss: 0.0000e+00 - total_loss: 56.5529 - val_top_k_categorical_accuracy: 0.0014 - val_loss: 392.6046 - val_regularization_loss: 0.0000e+00 - val_total_loss: 392.6046
Epoch 16/20
57/57 [==============================] - 32s 562ms/step - top_k_categorical_accuracy: 0.1525 - loss: 47.0925 - regularization_loss: 0.0000e+00 - total_loss: 47.0925 - val_top_k_categorical_accuracy: 0.0014 - val_loss: 396.5128 - val_regularization_loss: 0.0000e+00 - val_total_loss: 396.5128
Epoch 17/20
57/57 [==============================] - 32s 563ms/step - top_k_categorical_accuracy: 0.1428 - loss: 40.4092 - regularization_loss: 0.0000e+00 - total_loss: 40.4092 - val_top_k_categorical_accuracy: 0.0015 - val_loss: 401.1236 - val_regularization_loss: 0.0000e+00 - val_total_loss: 401.1236
Epoch 18/20
57/57 [==============================] - 33s 570ms/step - top_k_categorical_accuracy: 0.1411 - loss: 34.3613 - regularization_loss: 0.0000e+00 - total_loss: 34.3613 - val_top_k_categorical_accuracy: 0.0019 - val_loss: 403.2057 - val_regularization_loss: 0.0000e+00 - val_total_loss: 403.2057
Epoch 19/20
57/57 [==============================] - 33s 575ms/step - top_k_categorical_accuracy: 0.1414 - loss: 29.4971 - regularization_loss: 0.0000e+00 - total_loss: 29.4971 - val_top_k_categorical_accuracy: 0.0018 - val_loss: 406.6922 - val_regularization_loss: 0.0000e+00 - val_total_loss: 406.6922
Epoch 20/20
57/57 [==============================] - 32s 564ms/step - top_k_categorical_accuracy: 0.1374 - loss: 25.4933 - regularization_loss: 0.0000e+00 - total_loss: 25.4933 - val_top_k_categorical_accuracy: 0.0019 - val_loss: 409.6847 - val_regularization_loss: 0.0000e+00 - val_total_loss: 409.6847
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 Starting Evaluation
57/57 [==============================] - 10s 176ms/step - top_k_categorical_accuracy: 0.0000e+00 - loss: 139.8642 - regularization_loss: 0.0000e+00 - total_loss: 139.8642
MRR@10 0.0
MRR@20 0.0
MRR@50 0.0
MRR@100 0.0
Recall@10 0.0
	> Preprocessing Spotify dataset. This process may take several hours.
	>> Checking if the Spotify MPD is already preprocessed.
	>> Preprocessed version was found. Skipping stage.
	> Loading Spotify MPD dataset.
	>> Dataset loaded.
	> Splitting dataset
	>> Sampling 1% of dataset
	>> Splitting sample with 20% for testing
	>> Creating Vocab.
	> Building tensorflow datasets.
	>> Building training and validation dataset. This process may take some time.
	100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 7241/7241 [00:00<00:00, 7809.39it/s]
	100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 7241/7241 [00:00<00:00, 8587.54it/s]
	>> Building testing dataset. This process may take a long time.
	100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 1811/1811 [00:00<00:00, 8875.48it/s]
	Creating and compiling model
	> Retrieving candidates. This process takes more time as the number of unique items increases.
	Fitting model
	Epoch 1/20
	WARNING:tensorflow:The dtype of the source tensor must be floating (e.g. tf.float32) when calling GradientTape.gradient, got tf.int32
	WARNING:tensorflow:Gradients do not exist for variables ['counter:0'] when minimizing the loss.
	WARNING:tensorflow:The dtype of the source tensor must be floating (e.g. tf.float32) when calling GradientTape.gradient, got tf.int32
	WARNING:tensorflow:Gradients do not exist for variables ['counter:0'] when minimizing the loss.
	1/57 [..............................] - ETA: 0s - top_k_categorical_accuracy: 0.0000e+00 - loss: 621.1177 - regularization_loss: 0.0000e+00 - total_loss: 621.1177WARNING:tensorflow:From C:\Users\lmd-pc-03\anaconda3\lib\site-packages\tensorflow\python\ops\summary_ops_v2.py:1277: stop (from tensorflow.python.eager.profiler) is deprecated and will be removed after 2020-07-01.
	Instructions for updating:
	use `tf.profiler.experimental.stop` instead.
	2/57 [>.............................] - ETA: 1:00 - top_k_categorical_accuracy: 0.0000e+00 - loss: 621.0904 - regularization_loss: 0.0000e+00 - total_loss: 621.0904WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.4531s vs `on_train_batch_end` time: 1.7454s). Check your callbacks.
	57/57 [==============================] - 34s 596ms/step - top_k_categorical_accuracy: 0.0000e+00 - loss: 610.4406 - regularization_loss: 0.0000e+00 - total_loss: 610.4406 - val_top_k_categorical_accuracy: 2.7620e-04 - val_loss: 313.2148 - val_regularization_loss: 0.0000e+00 - val_total_loss: 313.2148
	Epoch 2/20
	57/57 [==============================] - 32s 554ms/step - top_k_categorical_accuracy: 0.0059 - loss: 607.5602 - regularization_loss: 0.0000e+00 - total_loss: 607.5602 - val_top_k_categorical_accuracy: 0.0000e+00 - val_loss: 312.8128 - val_regularization_loss: 0.0000e+00 - val_total_loss: 312.8128
	Epoch 3/20
	57/57 [==============================] - 32s 555ms/step - top_k_categorical_accuracy: 6.9051e-04 - loss: 592.5384 - regularization_loss: 0.0000e+00 - total_loss: 592.5384 - val_top_k_categorical_accuracy: 5.5241e-04 - val_loss: 313.7579 - val_regularization_loss: 0.0000e+00 - val_total_loss: 313.7579
	Epoch 4/20
	57/57 [==============================] - 32s 561ms/step - top_k_categorical_accuracy: 0.0019 - loss: 540.0459 - regularization_loss: 0.0000e+00 - total_loss: 540.0459 - val_top_k_categorical_accuracy: 9.6672e-04 - val_loss: 316.1017 - val_regularization_loss: 0.0000e+00 - val_total_loss: 316.1017
	Epoch 5/20
	57/57 [==============================] - 32s 569ms/step - top_k_categorical_accuracy: 0.0054 - loss: 459.0720 - regularization_loss: 0.0000e+00 - total_loss: 459.0720 - val_top_k_categorical_accuracy: 1.3810e-04 - val_loss: 323.2471 - val_regularization_loss: 0.0000e+00 - val_total_loss: 323.2471
	Epoch 6/20
	57/57 [==============================] - 32s 562ms/step - top_k_categorical_accuracy: 0.0134 - loss: 383.0080 - regularization_loss: 0.0000e+00 - total_loss: 383.0080 - val_top_k_categorical_accuracy: 5.5241e-04 - val_loss: 328.9191 - val_regularization_loss: 0.0000e+00 - val_total_loss: 328.9191
	Epoch 7/20
	57/57 [==============================] - 32s 559ms/step - top_k_categorical_accuracy: 0.0275 - loss: 318.0599 - regularization_loss: 0.0000e+00 - total_loss: 318.0599 - val_top_k_categorical_accuracy: 4.1431e-04 - val_loss: 340.4540 - val_regularization_loss: 0.0000e+00 - val_total_loss: 340.4540
	Epoch 8/20
	57/57 [==============================] - 32s 562ms/step - top_k_categorical_accuracy: 0.0441 - loss: 261.4458 - regularization_loss: 0.0000e+00 - total_loss: 261.4458 - val_top_k_categorical_accuracy: 2.7620e-04 - val_loss: 344.6627 - val_regularization_loss: 0.0000e+00 - val_total_loss: 344.6627
	Epoch 9/20
	57/57 [==============================] - 32s 559ms/step - top_k_categorical_accuracy: 0.0594 - loss: 212.6684 - regularization_loss: 0.0000e+00 - total_loss: 212.6684 - val_top_k_categorical_accuracy: 6.9051e-04 - val_loss: 352.9798 - val_regularization_loss: 0.0000e+00 - val_total_loss: 352.9798
	Epoch 10/20
	57/57 [==============================] - 32s 560ms/step - top_k_categorical_accuracy: 0.0853 - loss: 170.4088 - regularization_loss: 0.0000e+00 - total_loss: 170.4088 - val_top_k_categorical_accuracy: 6.9051e-04 - val_loss: 361.4565 - val_regularization_loss: 0.0000e+00 - val_total_loss: 361.4565
	Epoch 11/20
	57/57 [==============================] - 32s 561ms/step - top_k_categorical_accuracy: 0.1027 - loss: 135.3549 - regularization_loss: 0.0000e+00 - total_loss: 135.3549 - val_top_k_categorical_accuracy: 9.6672e-04 - val_loss: 371.1420 - val_regularization_loss: 0.0000e+00 - val_total_loss: 371.1420
	Epoch 12/20
	57/57 [==============================] - 33s 572ms/step - top_k_categorical_accuracy: 0.1159 - loss: 107.3723 - regularization_loss: 0.0000e+00 - total_loss: 107.3723 - val_top_k_categorical_accuracy: 0.0015 - val_loss: 376.0966 - val_regularization_loss: 0.0000e+00 - val_total_loss: 376.0966
	Epoch 13/20
	57/57 [==============================] - 33s 573ms/step - top_k_categorical_accuracy: 0.1334 - loss: 85.8168 - regularization_loss: 0.0000e+00 - total_loss: 85.8168 - val_top_k_categorical_accuracy: 0.0018 - val_loss: 383.9549 - val_regularization_loss: 0.0000e+00 - val_total_loss: 383.9549
	Epoch 14/20
	57/57 [==============================] - 34s 596ms/step - top_k_categorical_accuracy: 0.1280 - loss: 69.2909 - regularization_loss: 0.0000e+00 - total_loss: 69.2909 - val_top_k_categorical_accuracy: 0.0019 - val_loss: 384.4878 - val_regularization_loss: 0.0000e+00 - val_total_loss: 384.4878
	Epoch 15/20
	57/57 [==============================] - 32s 564ms/step - top_k_categorical_accuracy: 0.1480 - loss: 56.5529 - regularization_loss: 0.0000e+00 - total_loss: 56.5529 - val_top_k_categorical_accuracy: 0.0014 - val_loss: 392.6046 - val_regularization_loss: 0.0000e+00 - val_total_loss: 392.6046
	Epoch 16/20
	57/57 [==============================] - 32s 562ms/step - top_k_categorical_accuracy: 0.1525 - loss: 47.0925 - regularization_loss: 0.0000e+00 - total_loss: 47.0925 - val_top_k_categorical_accuracy: 0.0014 - val_loss: 396.5128 - val_regularization_loss: 0.0000e+00 - val_total_loss: 396.5128
	Epoch 17/20
	57/57 [==============================] - 32s 563ms/step - top_k_categorical_accuracy: 0.1428 - loss: 40.4092 - regularization_loss: 0.0000e+00 - total_loss: 40.4092 - val_top_k_categorical_accuracy: 0.0015 - val_loss: 401.1236 - val_regularization_loss: 0.0000e+00 - val_total_loss: 401.1236
	Epoch 18/20
	57/57 [==============================] - 33s 570ms/step - top_k_categorical_accuracy: 0.1411 - loss: 34.3613 - regularization_loss: 0.0000e+00 - total_loss: 34.3613 - val_top_k_categorical_accuracy: 0.0019 - val_loss: 403.2057 - val_regularization_loss: 0.0000e+00 - val_total_loss: 403.2057
	Epoch 19/20
	57/57 [==============================] - 33s 575ms/step - top_k_categorical_accuracy: 0.1414 - loss: 29.4971 - regularization_loss: 0.0000e+00 - total_loss: 29.4971 - val_top_k_categorical_accuracy: 0.0018 - val_loss: 406.6922 - val_regularization_loss: 0.0000e+00 - val_total_loss: 406.6922
	Epoch 20/20
	57/57 [==============================] - 32s 564ms/step - top_k_categorical_accuracy: 0.1374 - loss: 25.4933 - regularization_loss: 0.0000e+00 - total_loss: 25.4933 - val_top_k_categorical_accuracy: 0.0019 - val_loss: 409.6847 - val_regularization_loss: 0.0000e+00 - val_total_loss: 409.6847
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	Starting Evaluation
	57/57 [==============================] - 10s 176ms/step - top_k_categorical_accuracy: 0.0000e+00 - loss: 139.8642 - regularization_loss: 0.0000e+00 - total_loss: 139.8642
	MRR@10 0.0
	MRR@20 0.0
	MRR@50 0.0
	MRR@100 0.0
	Recall@10 0.0