Regression for high-level timbral features - Keras NN 7 filters model.ipynb

{"cells":[{"cell_type":"code","source":["pip install mirdata"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"CEBMcOkW5dyQ","executionInfo":{"status":"ok","timestamp":1679837550959,"user_tz":-120,"elapsed":21765,"user":{"displayName":"MATTEO FABBRI","userId":"11369581659480367990"}},"outputId":"8f978124-f975-4866-a2a9-88143c7ece2c"},"execution_count":1,"outputs":[{"output_type":"stream","name":"stdout","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Collecting mirdata\n","  Downloading mirdata-0.3.7-py3-none-any.whl (14.9 MB)\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m14.9/14.9 MB\u001b[0m \u001b[31m45.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hRequirement already satisfied: smart-open>=5.0.0 in /usr/local/lib/python3.9/dist-packages (from mirdata) (6.3.0)\n","Requirement already satisfied: requests in /usr/local/lib/python3.9/dist-packages 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pretty-midi-0.2.10\n"]}]},{"cell_type":"code","execution_count":2,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":36442,"status":"ok","timestamp":1679837587394,"user":{"displayName":"MATTEO FABBRI","userId":"11369581659480367990"},"user_tz":-120},"id":"RbpAjqCULUiQ","outputId":"d2d0e4c2-a2a8-4ec9-b3e4-07822237255b"},"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/gdrive\n"]}],"source":["import pandas\n","import os \n","import pickle\n","\n","import time\n","\n","import mirdata\n","import numpy as np\n","import tensorflow as tf\n","\n","from sklearn.model_selection import train_test_split\n","from sklearn.preprocessing import StandardScaler\n","\n","from keras.models import Sequential\n","from keras.layers import Input\n","from keras.layers import Dense\n","from keras.layers import Conv1D\n","from keras.layers import Flatten\n","from keras.layers import Dropout\n","from keras.layers import MaxPooling1D\n","\n","from google.colab import drive\n","drive.mount('/content/gdrive')"]},{"cell_type":"code","source":["# GLOBAL PATHS and variables\n","pickleFiles_FolderName = '/content/gdrive/MyDrive/Colab Notebooks/Music Information Retrieval/Assignment/Final'"],"metadata":{"id":"FWF8RwZCL64w","executionInfo":{"status":"ok","timestamp":1679837587395,"user_tz":-120,"elapsed":22,"user":{"displayName":"MATTEO FABBRI","userId":"11369581659480367990"}}},"execution_count":3,"outputs":[]},{"cell_type":"code","source":["tensF_ShuffleSeed = 0\n","train_split = 0.8 # validation split is the same as train_split"],"metadata":{"id":"RuqWXqC_jlcc","executionInfo":{"status":"ok","timestamp":1679837587396,"user_tz":-120,"elapsed":20,"user":{"displayName":"MATTEO FABBRI","userId":"11369581659480367990"}}},"execution_count":4,"outputs":[]},{"cell_type":"code","source":["# https://mirdata.readthedocs.io/en/stable/source/mirdata.html#module-mirdata.datasets.freesound_one_shot_percussive_sounds\n","\n","percDataset = mirdata.initialize(\"freesound_one_shot_percussive_sounds\")\n","percDataset.download()  \n","print('Finished downloading freesound_one_shot_percussive_sounds dataset.')\n","percDataset.validate() \n","print('Finished validating freesound_one_shot_percussive_sounds dataset.')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"JBvB8aJMA4-L","executionInfo":{"status":"ok","timestamp":1679837598640,"user_tz":-120,"elapsed":11263,"user":{"displayName":"MATTEO FABBRI","userId":"11369581659480367990"}},"outputId":"45a77611-f931-42a3-b639-d444178ab6cb"},"execution_count":5,"outputs":[{"output_type":"stream","name":"stderr","text":["107MB [00:01, 69.6MB/s]                          \n","5.35MB [00:00, 6.81MB/s]                          \n","57.3MB [00:01, 54.0MB/s]                            \n","1.29MB [00:00, 1.93MB/s]                          \n","8.00kB [00:00, 11.3kB/s]\n"]},{"output_type":"stream","name":"stdout","text":["Finished downloading freesound_one_shot_percussive_sounds dataset.\n"]},{"output_type":"stream","name":"stderr","text":["100%|██████████| 1/1 [00:00<00:00, 221.06it/s]\n","100%|██████████| 10254/10254 [00:01<00:00, 6839.38it/s]"]},{"output_type":"stream","name":"stdout","text":["Finished validating freesound_one_shot_percussive_sounds dataset.\n"]},{"output_type":"stream","name":"stderr","text":["\n"]}]},{"cell_type":"code","source":["# Find min and max values of timbral synthesis features annotations, \n","# so that we can normalize later\n","with open(os.path.join(pickleFiles_FolderName, 'audioFileWavef_TimbralFeatures_DF.p'), 'rb') as audioFileWavef_TimbralFeatures_DF_Pickle:\n","  audioFileWavef_TimbralFeatures_DF = pickle.load(audioFileWavef_TimbralFeatures_DF_Pickle)\n","\n","audioFileWavef_TimbralFeatures_DF.head() # successfully retrieved pandas dataframe from pickle file"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":299},"id":"qaiaLNfrJ8uy","executionInfo":{"status":"ok","timestamp":1679837611991,"user_tz":-120,"elapsed":13356,"user":{"displayName":"MATTEO FABBRI","userId":"11369581659480367990"}},"outputId":"5d47d202-287c-445d-d82e-1f33cdc8393e"},"execution_count":6,"outputs":[{"output_type":"execute_result","data":{"text/plain":["   freesound_sound_id  audio_waveform_sampleN_0  audio_waveform_sampleN_1  \\\n","0              203887                 -0.000031                 -0.000031   \n","1              203893                 -0.000031                 -0.000031   \n","2              319613                  0.023254                 -0.362274   \n","3              361958                 -0.003174                 -0.002441   \n","4              277684                  0.001007                  0.001984   \n","\n","   audio_waveform_sampleN_2  audio_waveform_sampleN_3  \\\n","0                  0.000000                 -0.000031   \n","1                 -0.000031                 -0.000031   \n","2                 -0.115540                  0.606995   \n","3                 -0.009338                 -0.005646   \n","4                  0.002197                  0.002533   \n","\n","   audio_waveform_sampleN_4  audio_waveform_sampleN_5  \\\n","0                 -0.000031                 -0.000031   \n","1                  0.000000                  0.000000   \n","2                  0.025940                 -0.159943   \n","3                 -0.014252                 -0.008392   \n","4                  0.002716                  0.002594   \n","\n","   audio_waveform_sampleN_6  audio_waveform_sampleN_7  \\\n","0                  0.000000                  0.000031   \n","1                  0.000000                  0.000031   \n","2                  0.004211                 -0.385834   \n","3                 -0.015869                  0.002869   \n","4                  0.002838                  0.003174   \n","\n","   audio_waveform_sampleN_8  ...  audio_waveform_sampleN_15997  \\\n","0                  0.000031  ...                           0.0   \n","1                  0.000061  ...                           0.0   \n","2                  0.056091  ...                           0.0   \n","3                  0.007721  ...                           0.0   \n","4                  0.002899  ...                           0.0   \n","\n","   audio_waveform_sampleN_15998  audio_waveform_sampleN_15999   hardness  \\\n","0                           0.0                           0.0  58.964800   \n","1                           0.0                           0.0  68.950243   \n","2                           0.0                           0.0  70.543572   \n","3                           0.0                           0.0  54.433781   \n","4                           0.0                           0.0  71.738502   \n","\n","       depth  brightness  roughness     warmth  sharpness  boominess  \n","0  52.583343   51.286288  54.450442  53.450765  43.850985  36.458663  \n","1  35.996983   71.406639  67.818761  34.422775  58.811990  21.432983  \n","2  54.408973   62.367384  55.920217  39.524543  53.265036  37.984857  \n","3  70.611045   38.445561  43.515676  55.998536  40.757533  43.517493  \n","4  57.402842   73.531486  50.322603  36.656637  56.843763  28.371194  \n","\n","[5 rows x 16008 columns]"],"text/html":["\n","  <div id=\"df-ff54c273-bd65-458e-b2be-3945b5ef8172\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>freesound_sound_id</th>\n","      <th>audio_waveform_sampleN_0</th>\n","      <th>audio_waveform_sampleN_1</th>\n","      <th>audio_waveform_sampleN_2</th>\n","      <th>audio_waveform_sampleN_3</th>\n","      <th>audio_waveform_sampleN_4</th>\n","      <th>audio_waveform_sampleN_5</th>\n","      <th>audio_waveform_sampleN_6</th>\n","      <th>audio_waveform_sampleN_7</th>\n","      <th>audio_waveform_sampleN_8</th>\n","      <th>...</th>\n","      <th>audio_waveform_sampleN_15997</th>\n","      <th>audio_waveform_sampleN_15998</th>\n","      <th>audio_waveform_sampleN_15999</th>\n","      <th>hardness</th>\n","      <th>depth</th>\n","      <th>brightness</th>\n","      <th>roughness</th>\n","      <th>warmth</th>\n","      <th>sharpness</th>\n","      <th>boominess</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>203887</td>\n","      <td>-0.000031</td>\n","      <td>-0.000031</td>\n","      <td>0.000000</td>\n","      <td>-0.000031</td>\n","      <td>-0.000031</td>\n","      <td>-0.000031</td>\n","      <td>0.000000</td>\n","      <td>0.000031</td>\n","      <td>0.000031</td>\n","      <td>...</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>58.964800</td>\n","      <td>52.583343</td>\n","      <td>51.286288</td>\n","      <td>54.450442</td>\n","      <td>53.450765</td>\n","      <td>43.850985</td>\n","      <td>36.458663</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>203893</td>\n","      <td>-0.000031</td>\n","      <td>-0.000031</td>\n","      <td>-0.000031</td>\n","      <td>-0.000031</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000031</td>\n","      <td>0.000061</td>\n","      <td>...</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>68.950243</td>\n","      <td>35.996983</td>\n","      <td>71.406639</td>\n","      <td>67.818761</td>\n","      <td>34.422775</td>\n","      <td>58.811990</td>\n","      <td>21.432983</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>319613</td>\n","      <td>0.023254</td>\n","      <td>-0.362274</td>\n","      <td>-0.115540</td>\n","      <td>0.606995</td>\n","      <td>0.025940</td>\n","      <td>-0.159943</td>\n","      <td>0.004211</td>\n","      <td>-0.385834</td>\n","      <td>0.056091</td>\n","      <td>...</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>70.543572</td>\n","      <td>54.408973</td>\n","      <td>62.367384</td>\n","      <td>55.920217</td>\n","      <td>39.524543</td>\n","      <td>53.265036</td>\n","      <td>37.984857</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>361958</td>\n","      <td>-0.003174</td>\n","      <td>-0.002441</td>\n","      <td>-0.009338</td>\n","      <td>-0.005646</td>\n","      <td>-0.014252</td>\n","      <td>-0.008392</td>\n","      <td>-0.015869</td>\n","      <td>0.002869</td>\n","      <td>0.007721</td>\n","      <td>...</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>54.433781</td>\n","      <td>70.611045</td>\n","      <td>38.445561</td>\n","      <td>43.515676</td>\n","      <td>55.998536</td>\n","      <td>40.757533</td>\n","      <td>43.517493</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>277684</td>\n","      <td>0.001007</td>\n","      <td>0.001984</td>\n","      <td>0.002197</td>\n","      <td>0.002533</td>\n","      <td>0.002716</td>\n","      <td>0.002594</td>\n","      <td>0.002838</td>\n","      <td>0.003174</td>\n","      <td>0.002899</td>\n","      <td>...</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>71.738502</td>\n","      <td>57.402842</td>\n","      <td>73.531486</td>\n","      <td>50.322603</td>\n","      <td>36.656637</td>\n","      <td>56.843763</td>\n","      <td>28.371194</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>5 rows × 16008 columns</p>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ff54c273-bd65-458e-b2be-3945b5ef8172')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    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FABBRI","userId":"11369581659480367990"}}},"execution_count":7,"outputs":[]},{"cell_type":"code","source":["timbFeat_MinList = audioFileWavef_TimbralFeatures_DF[timbFeat_List].min(axis = 0, numeric_only = True)\n","timbFeat_MaxList = audioFileWavef_TimbralFeatures_DF[timbFeat_List].max(axis = 0, numeric_only = True)"],"metadata":{"id":"PZdazT3aMhBc","executionInfo":{"status":"ok","timestamp":1679837611992,"user_tz":-120,"elapsed":10,"user":{"displayName":"MATTEO FABBRI","userId":"11369581659480367990"}}},"execution_count":8,"outputs":[]},{"cell_type":"code","source":["print(timbFeat_MinList)\n","print(timbFeat_MaxList)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"2rIA8PPsNgPf","executionInfo":{"status":"ok","timestamp":1679837611992,"user_tz":-120,"elapsed":9,"user":{"displayName":"MATTEO FABBRI","userId":"11369581659480367990"}},"outputId":"81b12f33-aeab-4fad-fd03-6fe8b2957e0e"},"execution_count":9,"outputs":[{"output_type":"stream","name":"stdout","text":["hardness      11.586026\n","depth          0.140980\n","brightness    10.874446\n","roughness      0.000000\n","warmth         7.696248\n","sharpness      0.000000\n","boominess      0.000000\n","dtype: float64\n","hardness       82.596457\n","depth         100.000000\n","brightness     84.309024\n","roughness      77.090665\n","warmth         78.870422\n","sharpness      92.585410\n","boominess      71.482249\n","dtype: float64\n"]}]},{"cell_type":"code","source":["timbFeat_List = [\"ac_hardness\", \"ac_depth\", \"ac_brightness\", \"ac_roughness\", \"ac_warmth\", \"ac_sharpness\", \"ac_boominess\"]"],"metadata":{"id":"G5AcQybyEV13","executionInfo":{"status":"ok","timestamp":1679837611993,"user_tz":-120,"elapsed":7,"user":{"displayName":"MATTEO FABBRI","userId":"11369581659480367990"}}},"execution_count":10,"outputs":[]},{"cell_type":"code","source":["# https://mirdata.readthedocs.io/en/stable/source/tutorial.html\n","\n","def freeSound_PercDataset_generator():\n","    track_ids = percDataset.track_ids\n","    for track_id in track_ids:\n","      track = percDataset.track(track_id)\n","      audio_signal, _ = track.audio\n","      audioSignal = np.array(audio_signal)\n","      audioSignal.resize(16000, refcheck=False)\n","      # print(len(audioSignal))\n","      # consider training example as valid only if all annotations are present\n","      # (there are some missing keys in the mir_data track.audiocommons_analysis dict)\n","      timbFeaturesGroundTruth = []\n","      for index, timbFeatName in enumerate(timbFeat_List):\n","        # apply normalization between 0 and 1 (y = x-min(x) / max(x) - min(x))\n","        if timbFeatName in track.audiocommons_analysis.keys():\n","          normValue = (track.audiocommons_analysis[timbFeatName] - timbFeat_MinList[index]) / (timbFeat_MaxList[index] - timbFeat_MinList[index])\n","          timbFeaturesGroundTruth.append(np.float32(normValue))\n","        else:\n","          timbFeaturesGroundTruth.append(0.)\n","      # Keras' Input layer accepts either 2 different 'datasets' for x (examples) and y (ground truth),\n","      # or a single tuple object as (x, y)\n","      # https://keras.io/api/models/model_training_apis/ \n","      yield (audioSignal,\n","              timbFeaturesGroundTruth)\n","\n","dataset = tf.data.Dataset.from_generator(\n","    freeSound_PercDataset_generator,\n","    output_signature=(\n","         tf.TensorSpec(shape = (16000), dtype = tf.float32, name = 'audio_waveform'),\n","         tf.TensorSpec(shape = (7), dtype = tf.float32, name = 'annotations')))"],"metadata":{"id":"-lko-5hw6N78","executionInfo":{"status":"ok","timestamp":1679837616162,"user_tz":-120,"elapsed":4176,"user":{"displayName":"MATTEO FABBRI","userId":"11369581659480367990"}}},"execution_count":11,"outputs":[]},{"cell_type":"code","source":["dataset"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"x9I9OmbvOrkV","executionInfo":{"status":"ok","timestamp":1679837616162,"user_tz":-120,"elapsed":10,"user":{"displayName":"MATTEO FABBRI","userId":"11369581659480367990"}},"outputId":"29d39d2f-bf9e-4e7a-d9af-4d06b1602ccb"},"execution_count":12,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<FlatMapDataset element_spec=(TensorSpec(shape=(16000,), dtype=tf.float32, name='audio_waveform'), TensorSpec(shape=(7,), dtype=tf.float32, name='annotations'))>"]},"metadata":{},"execution_count":12}]},{"cell_type":"code","source":["# check dataset \n","#for element in dataset:\n","#  print(element)"],"metadata":{"id":"poLnP0se4haF","executionInfo":{"status":"ok","timestamp":1679837616163,"user_tz":-120,"elapsed":8,"user":{"displayName":"MATTEO FABBRI","userId":"11369581659480367990"}}},"execution_count":13,"outputs":[]},{"cell_type":"code","source":["# Shuffle entire dataset and split it into train/validation/test datasets\n","\n","num_elements = len(list(dataset))\n","print(f'There are {num_elements} elements in the Dataset')\n","\n","dataset = dataset.shuffle(num_elements, seed=tensF_ShuffleSeed)\n","    \n","train_size = int(train_split * num_elements)\n","test_size = int((1. - train_split) * num_elements)\n","    \n","test_ds = dataset.skip(train_size).take(test_size)\n","\n","train_size_withValSplit = int(train_split * train_size)\n","validation_size = int((1. - train_split) * train_size)\n","\n","train_ds = dataset.take(train_size_withValSplit)    \n","validation_ds = dataset.skip(train_size_withValSplit).take(validation_size)\n","\n","print(f'There are {len(list(train_ds))} elements in the training Dataset')\n","print(f'There are {len(list(validation_ds))} elements in the validation Dataset')\n","print(f'There are {len(list(test_ds))} elements in the test Dataset')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"9v2Ho2pqjTtL","executionInfo":{"status":"ok","timestamp":1679837667108,"user_tz":-120,"elapsed":50952,"user":{"displayName":"MATTEO FABBRI","userId":"11369581659480367990"}},"outputId":"b12e10bd-ce0a-4bfe-eef4-416a649a8787"},"execution_count":14,"outputs":[{"output_type":"stream","name":"stdout","text":["There are 10254 elements in the Dataset\n","There are 6562 elements in the training Dataset\n","There are 1640 elements in the validation Dataset\n","There are 2050 elements in the test Dataset\n"]}]},{"cell_type":"code","source":["batch_size = 1"],"metadata":{"id":"F0syowcfzl_v","executionInfo":{"status":"ok","timestamp":1679837667109,"user_tz":-120,"elapsed":21,"user":{"displayName":"MATTEO FABBRI","userId":"11369581659480367990"}}},"execution_count":15,"outputs":[]},{"cell_type":"code","source":["train_ds = train_ds.batch(batch_size)\n","validation_ds = validation_ds.batch(batch_size)\n","test_ds = test_ds.batch(batch_size)"],"metadata":{"id":"cG2JGPybRUh0","executionInfo":{"status":"ok","timestamp":1679837667110,"user_tz":-120,"elapsed":20,"user":{"displayName":"MATTEO FABBRI","userId":"11369581659480367990"}}},"execution_count":16,"outputs":[]},{"cell_type":"code","execution_count":17,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":19,"status":"ok","timestamp":1679837667110,"user":{"displayName":"MATTEO FABBRI","userId":"11369581659480367990"},"user_tz":-120},"id":"Olbeoyxm23wI","outputId":"164d04ff-32d3-4631-ab0b-8625190eb771"},"outputs":[{"output_type":"stream","name":"stdout","text":["Model: \"sequential\"\n","_________________________________________________________________\n"," Layer (type)                Output Shape              Param #   \n","=================================================================\n"," Conv1D_1 (Conv1D)           (1, 15994, 7)             56        \n","                                                                 \n"," MaxPooling_1D_1 (MaxPooling  (1, 7997, 7)             0         \n"," 1D)                                                             \n","                                                                 \n"," Conv1D_2 (Conv1D)           (1, 7991, 7)              350       \n","                                                                 \n"," MaxPooling_1D_2 (MaxPooling  (1, 3995, 7)             0         \n"," 1D)                                                             \n","                                                                 \n"," Conv1D_3 (Conv1D)           (1, 3989, 7)              350       \n","                                                                 \n"," MaxPooling_1D_3 (MaxPooling  (1, 1994, 7)             0         \n"," 1D)                                                             \n","                                                                 \n"," Conv1D_4 (Conv1D)           (1, 1988, 7)              350       \n","                                                                 \n"," MaxPooling_1D_4 (MaxPooling  (1, 994, 7)              0         \n"," 1D)                                                             \n","                                                                 \n"," Conv1D_5 (Conv1D)           (1, 988, 7)               350       \n","                                                                 \n"," MaxPooling_1D_5 (MaxPooling  (1, 494, 7)              0         \n"," 1D)                                                             \n","                                                                 \n"," flatten (Flatten)           (1, 3458)                 0         \n","                                                                 \n"," Dense_1 (Dense)             (1, 100)                  345900    \n","                                                                 \n"," Dense_3 (Dense)             (1, 7)                    707       \n","                                                                 \n","=================================================================\n","Total params: 348,063\n","Trainable params: 348,063\n","Non-trainable params: 0\n","_________________________________________________________________\n"]}],"source":["model = Sequential()\n","model.add(Input(shape = (16000, 1), batch_size = batch_size))\n","\n","# with stride 1, the output size == input size - filter size + 1 (16000 - 7 + 1)\n","model.add(Conv1D(7, 7, strides = 1, activation=\"relu\", padding = 'valid', name = \"Conv1D_1\"))\n","model.add(MaxPooling1D(pool_size = 2, name = \"MaxPooling_1D_1\"))\n","\n","model.add(Conv1D(7, 7, strides = 1, activation=\"relu\", padding = 'valid', name = \"Conv1D_2\"))\n","model.add(MaxPooling1D(pool_size = 2, name = \"MaxPooling_1D_2\"))\n","\n","model.add(Conv1D(7, 7, strides = 1, activation=\"relu\", padding = 'valid', name = \"Conv1D_3\"))\n","model.add(MaxPooling1D(pool_size = 2, name = \"MaxPooling_1D_3\"))\n","\n","model.add(Conv1D(7, 7, strides = 1, activation=\"relu\", padding = 'valid', name = \"Conv1D_4\"))\n","model.add(MaxPooling1D(pool_size = 2, name = \"MaxPooling_1D_4\"))\n","\n","model.add(Conv1D(7, 7, strides = 1, activation=\"relu\", padding = 'valid', name = \"Conv1D_5\"))\n","model.add(MaxPooling1D(pool_size = 2, name = \"MaxPooling_1D_5\"))\n","\n","model.add(Flatten())\n","model.add(Dense(100, activation = 'relu', name = \"Dense_1\"))\n","\n","model.add(Dense(7, activation = 'linear', name = \"Dense_3\"))\n","\n","# In a regression task, since the outputs and ground truth values are continuous values,\n","# rather than classes, and hence ezact prediction are not possible, accuracy is not used \n","# https://www.freecodecamp.org/news/evaluation-metrics-for-regression-problems-machine-learning/\n","# https://keras.io/api/metrics/\n","_metrics = ['mean_absolute_error', 'mean_squared_error']\n","model.compile(loss = 'mean_absolute_error', loss_weights = [1/7,1/7,1/7,1/7,1/7,1/7,1/7], optimizer = 'adam', metrics = _metrics)\n","model.summary()"]},{"cell_type":"code","source":["pngFilePath = '/content/gdrive/MyDrive/Colab Notebooks/Music Information Retrieval/Assignment/Final/seven-filters_model_plot.png'\n","tf.keras.utils.plot_model(model, to_file = pngFilePath, show_shapes = True, show_dtype = True, show_layer_names = True, rankdir = 'LR', dpi = 96, show_layer_activations = True)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":47},"id":"OhTid-s7HdO4","executionInfo":{"status":"ok","timestamp":1679837667677,"user_tz":-120,"elapsed":573,"user":{"displayName":"MATTEO FABBRI","userId":"11369581659480367990"}},"outputId":"51f7b81e-0325-465e-b3c5-cea8619ded24"},"execution_count":18,"outputs":[{"output_type":"execute_result","data":{"image/png":"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\n","text/plain":["<IPython.core.display.Image object>"]},"metadata":{},"execution_count":18}]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"n6qV_e4vp8fn","executionInfo":{"status":"ok","timestamp":1679597081515,"user_tz":-60,"elapsed":1302425,"user":{"displayName":"MATTEO FABBRI","userId":"11369581659480367990"}},"outputId":"353abd3f-7a34-42c3-d31c-b1f26097857b"},"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/20\n","6562/6562 [==============================] - 72s 8ms/step - loss: 0.0130 - mean_absolute_error: 0.0911 - mean_squared_error: 0.0155 - val_loss: 0.0102 - val_mean_absolute_error: 0.0711 - val_mean_squared_error: 0.0102\n","Epoch 2/20\n","6562/6562 [==============================] - 61s 8ms/step - loss: 0.0100 - mean_absolute_error: 0.0702 - mean_squared_error: 0.0096 - val_loss: 0.0094 - val_mean_absolute_error: 0.0658 - val_mean_squared_error: 0.0087\n","Epoch 3/20\n","6562/6562 [==============================] - 61s 8ms/step - loss: 0.0092 - mean_absolute_error: 0.0645 - mean_squared_error: 0.0082 - val_loss: 0.0080 - val_mean_absolute_error: 0.0562 - val_mean_squared_error: 0.0066\n","Epoch 4/20\n","6562/6562 [==============================] - 62s 8ms/step - loss: 0.0088 - mean_absolute_error: 0.0617 - mean_squared_error: 0.0076 - val_loss: 0.0088 - val_mean_absolute_error: 0.0613 - val_mean_squared_error: 0.0078\n","Epoch 5/20\n","6562/6562 [==============================] - 60s 8ms/step - loss: 0.0083 - mean_absolute_error: 0.0584 - mean_squared_error: 0.0069 - val_loss: 0.0101 - val_mean_absolute_error: 0.0705 - val_mean_squared_error: 0.0106\n","Epoch 6/20\n","6562/6562 [==============================] - 60s 8ms/step - loss: 0.0081 - mean_absolute_error: 0.0568 - mean_squared_error: 0.0066 - val_loss: 0.0077 - val_mean_absolute_error: 0.0538 - val_mean_squared_error: 0.0062\n","Epoch 7/20\n","6562/6562 [==============================] - 62s 8ms/step - loss: 0.0078 - mean_absolute_error: 0.0548 - mean_squared_error: 0.0063 - val_loss: 0.0078 - val_mean_absolute_error: 0.0544 - val_mean_squared_error: 0.0062\n","Epoch 8/20\n","6562/6562 [==============================] - 60s 8ms/step - loss: 0.0077 - mean_absolute_error: 0.0540 - mean_squared_error: 0.0062 - val_loss: 0.0081 - val_mean_absolute_error: 0.0565 - val_mean_squared_error: 0.0066\n","Epoch 9/20\n","6562/6562 [==============================] - 62s 8ms/step - loss: 0.0077 - mean_absolute_error: 0.0538 - mean_squared_error: 0.0062 - val_loss: 0.0070 - val_mean_absolute_error: 0.0489 - val_mean_squared_error: 0.0050\n","Epoch 10/20\n","6562/6562 [==============================] - 60s 8ms/step - loss: 0.0075 - mean_absolute_error: 0.0527 - mean_squared_error: 0.0059 - val_loss: 0.0078 - val_mean_absolute_error: 0.0546 - val_mean_squared_error: 0.0063\n","Epoch 11/20\n","6562/6562 [==============================] - 60s 8ms/step - loss: 0.0074 - mean_absolute_error: 0.0519 - mean_squared_error: 0.0057 - val_loss: 0.0067 - val_mean_absolute_error: 0.0471 - val_mean_squared_error: 0.0048\n","Epoch 12/20\n","6562/6562 [==============================] - 62s 8ms/step - loss: 0.0073 - mean_absolute_error: 0.0513 - mean_squared_error: 0.0057 - val_loss: 0.0068 - val_mean_absolute_error: 0.0477 - val_mean_squared_error: 0.0052\n","Epoch 13/20\n","6562/6562 [==============================] - 60s 8ms/step - loss: 0.0073 - mean_absolute_error: 0.0510 - mean_squared_error: 0.0056 - val_loss: 0.0068 - val_mean_absolute_error: 0.0474 - val_mean_squared_error: 0.0048\n","Epoch 14/20\n","6562/6562 [==============================] - 61s 8ms/step - loss: 0.0072 - mean_absolute_error: 0.0503 - mean_squared_error: 0.0053 - val_loss: 0.0070 - val_mean_absolute_error: 0.0490 - val_mean_squared_error: 0.0052\n","Epoch 15/20\n","6562/6562 [==============================] - 61s 8ms/step - loss: 0.0071 - mean_absolute_error: 0.0500 - mean_squared_error: 0.0054 - val_loss: 0.0072 - val_mean_absolute_error: 0.0503 - val_mean_squared_error: 0.0056\n","Epoch 16/20\n","6562/6562 [==============================] - 61s 8ms/step - loss: 0.0070 - mean_absolute_error: 0.0492 - mean_squared_error: 0.0052 - val_loss: 0.0072 - val_mean_absolute_error: 0.0505 - val_mean_squared_error: 0.0053\n","Epoch 17/20\n","6562/6562 [==============================] - 61s 8ms/step - loss: 0.0070 - mean_absolute_error: 0.0491 - mean_squared_error: 0.0053 - val_loss: 0.0068 - val_mean_absolute_error: 0.0478 - val_mean_squared_error: 0.0051\n","Epoch 18/20\n","6562/6562 [==============================] - 61s 8ms/step - loss: 0.0069 - mean_absolute_error: 0.0486 - mean_squared_error: 0.0051 - val_loss: 0.0069 - val_mean_absolute_error: 0.0481 - val_mean_squared_error: 0.0049\n","Epoch 19/20\n","6562/6562 [==============================] - 61s 8ms/step - loss: 0.0069 - mean_absolute_error: 0.0481 - mean_squared_error: 0.0051 - val_loss: 0.0068 - val_mean_absolute_error: 0.0474 - val_mean_squared_error: 0.0050\n","Epoch 20/20\n","6562/6562 [==============================] - 71s 10ms/step - loss: 0.0069 - mean_absolute_error: 0.0481 - mean_squared_error: 0.0050 - val_loss: 0.0068 - val_mean_absolute_error: 0.0474 - val_mean_squared_error: 0.0046\n","Finished training. It took 1301.7397210209997 seconds\n"]}],"source":["start = time.monotonic()\n","history = model.fit(train_ds, epochs = 20, batch_size = batch_size, validation_data = validation_ds)\n","stop = time.monotonic()\n","print(f'Finished training. It took {stop - start} seconds')"]},{"cell_type":"code","source":["model.metrics_names"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"PoN6ptSlcfFf","executionInfo":{"status":"ok","timestamp":1679597081515,"user_tz":-60,"elapsed":22,"user":{"displayName":"MATTEO FABBRI","userId":"11369581659480367990"}},"outputId":"a9bb0a3f-d194-486a-e405-844e5d70f3dd"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["['loss', 'mean_absolute_error', 'mean_squared_error']"]},"metadata":{},"execution_count":19}]},{"cell_type":"code","source":["testLoss, mean_absolute_error, mean_squared_error = model.evaluate(test_ds, batch_size = batch_size)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"l3F-jEia2oA8","executionInfo":{"status":"ok","timestamp":1679597102208,"user_tz":-60,"elapsed":20697,"user":{"displayName":"MATTEO FABBRI","userId":"11369581659480367990"}},"outputId":"b262c8d0-1524-4382-ea6a-a0463d843d67"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["2050/2050 [==============================] - 15s 3ms/step - loss: 0.0067 - mean_absolute_error: 0.0470 - mean_squared_error: 0.0048\n"]}]},{"cell_type":"code","source":["print(type(testLoss))\n","\n","print(model.metrics_names)\n","print(testLoss)\n","print(mean_absolute_error)\n","print(mean_squared_error)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"VNN-wa0v3klB","executionInfo":{"status":"ok","timestamp":1679597102209,"user_tz":-60,"elapsed":29,"user":{"displayName":"MATTEO FABBRI","userId":"11369581659480367990"}},"outputId":"8f8804dd-d521-478b-e5e1-fd2cbbd2fbd5"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["<class 'float'>\n","['loss', 'mean_absolute_error', 'mean_squared_error']\n","0.006714077666401863\n","0.04699857160449028\n","0.004786747507750988\n"]}]},{"cell_type":"code","source":["# https://keras.io/guides/training_with_built_in_methods/\n","\n","import matplotlib.pyplot as plt\n","\n","def plot_loss_and_acc(history):\n","    train_loss = history.history[\"loss\"]\n","    val_loss = history.history[\"val_loss\"]\n","\n","    fig = plt.figure(figsize=(12,5))\n","\n","    ax = fig.add_subplot(121)\n","    ax.plot(range(len(train_loss)), train_loss, label=\"Train Loss\");\n","    plt.xlabel(\"Epochs\"); plt.ylabel(\"Loss (M. A. E.)\");\n","    plt.title(\"Train Loss (M. A. E.)\");\n","    plt.legend(loc=\"best\");\n","\n","    ax = fig.add_subplot(122)\n","    ax.plot(range(len(val_loss)), val_loss, label=\"Validation loss\");\n","    plt.xlabel(\"Epochs\"); plt.ylabel(\"Validation loss (M. A. E.)\");\n","    plt.title(\"Validation loss (M. A. E.)\");\n","    plt.legend(loc=\"best\");\n","\n","plot_loss_and_acc(history)\n","print(f'Test loss; {testLoss}')\n","\n","'''\n","Overfitting if: training loss << validation loss\n","Underfitting if: training loss >> validation loss\n","Just right if training loss ~ validation loss\n","'''"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":403},"id":"Rz2M6wJZcsRq","executionInfo":{"status":"ok","timestamp":1679597102795,"user_tz":-60,"elapsed":612,"user":{"displayName":"MATTEO FABBRI","userId":"11369581659480367990"}},"outputId":"7ed2d933-a104-4153-c0c7-dfc6744d8f0c"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Test loss; 0.006714077666401863\n"]},{"output_type":"execute_result","data":{"text/plain":["'\\nOverfitting if: training loss << validation loss\\nUnderfitting if: training loss >> validation loss\\nJust right if training loss ~ validation loss\\n'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":22},{"output_type":"display_data","data":{"text/plain":["<Figure size 864x360 with 2 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]}],"metadata":{"colab":{"provenance":[],"authorship_tag":"ABX9TyPXQ5eMnP2AURcn7wZup5b5"},"gpuClass":"standard","kernelspec":{"display_name":"Python 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