model_training.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "• Using TensorFlow Version:  2.8.0\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras.metrics import MeanSquaredError\n",
    "from tensorflow_addons.metrics.r_square import RSquare\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "print('\\u2022 Using TensorFlow Version: ', tf.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Folders & Paths\n",
    "audio_path = './audio/'\n",
    "\n",
    "features_path = './features/Normalized_Features.csv'\n",
    "\n",
    "truth_path = './truth/ratings/'\n",
    "aro_truth_path = truth_path + 'Arousal.csv'\n",
    "val_truth_path = truth_path + 'Valence.csv'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Read Feature set data & ground truth for Arousal & Valence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "features_norm = pd.read_csv(features_path)\n",
    "aro_truth = pd.read_csv(aro_truth_path, header=None)\n",
    "val_truth = pd.read_csv(val_truth_path, header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       0.523495\n",
       "1       0.211871\n",
       "2       0.589448\n",
       "3       0.396538\n",
       "4       0.764221\n",
       "          ...   \n",
       "1208    0.902721\n",
       "1209    0.655400\n",
       "1210    0.968673\n",
       "1211    0.812036\n",
       "1212    0.927453\n",
       "Name: 1, Length: 1213, dtype: float32"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert to float32 datatype (Meyda js outputs float32 data)\n",
    "aro_y = aro_truth[1].astype(np.float32)\n",
    "aro_y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      -0.767519\n",
       "1      -0.577906\n",
       "2      -0.752679\n",
       "3       0.526793\n",
       "4      -0.190437\n",
       "          ...   \n",
       "1208   -0.952185\n",
       "1209   -0.742786\n",
       "1210   -0.965375\n",
       "1211    0.022259\n",
       "1212   -0.592745\n",
       "Name: 1, Length: 1213, dtype: float32"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert to float32 datatype (Meyda js outputs float32 data)\n",
    "val_y = val_truth[1].astype(np.float32)\n",
    "val_y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of files: 1213\n"
     ]
    }
   ],
   "source": [
    "files_list = features_norm['fileName'].tolist()\n",
    "print(f'Number of files: {len(files_list)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reduce features to support features available in Meyda js\n",
    "meyda_available_features = ['rms','zcr','spectralRolloff','spectralCentroid','spectralSpread','spectralSkewness','spectralKurtosis','spectralFlatness','mfcc','chroma','loudness','energy','perceptualSharpness','spectralSlope']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of features: 14\n"
     ]
    }
   ],
   "source": [
    "# Meyda extractable features\n",
    "features = ['rms','zerocross','rolloff','centroid','spread','skewness','kurtosis','flatness','mfcc','chromagram','loudness','energy','perceptual_sharp','spectral_slope']\n",
    "# meyda_available_features = ['rms','zcr','spectralRolloff','spectralCentroid','spectralSpread','spectralSkewness','spectralKurtosis','spectralFlatness','mfcc','chroma','loudness','energy','perceptualSharpness','spectralSlope']\n",
    "\n",
    "print(f'Number of features: {len(features)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "74\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['rms_mean',\n",
       " 'rms_std',\n",
       " 'zerocross_mean',\n",
       " 'zerocross_std',\n",
       " 'rolloff_mean',\n",
       " 'rolloff_std',\n",
       " 'centroid_mean',\n",
       " 'centroid_std',\n",
       " 'spread_mean',\n",
       " 'spread_std',\n",
       " 'skewness_mean',\n",
       " 'skewness_std',\n",
       " 'kurtosis_mean',\n",
       " 'kurtosis_std',\n",
       " 'flatness_mean',\n",
       " 'flatness_std',\n",
       " 'mfcc_mean_1',\n",
       " 'mfcc_mean_2',\n",
       " 'mfcc_mean_3',\n",
       " 'mfcc_mean_4',\n",
       " 'mfcc_mean_5',\n",
       " 'mfcc_mean_6',\n",
       " 'mfcc_mean_7',\n",
       " 'mfcc_mean_8',\n",
       " 'mfcc_mean_9',\n",
       " 'mfcc_mean_10',\n",
       " 'mfcc_mean_11',\n",
       " 'mfcc_mean_12',\n",
       " 'mfcc_mean_13',\n",
       " 'mfcc_std_1',\n",
       " 'mfcc_std_2',\n",
       " 'mfcc_std_3',\n",
       " 'mfcc_std_4',\n",
       " 'mfcc_std_5',\n",
       " 'mfcc_std_6',\n",
       " 'mfcc_std_7',\n",
       " 'mfcc_std_8',\n",
       " 'mfcc_std_9',\n",
       " 'mfcc_std_10',\n",
       " 'mfcc_std_11',\n",
       " 'mfcc_std_12',\n",
       " 'mfcc_std_13',\n",
       " 'chromagram_mean_1',\n",
       " 'chromagram_mean_2',\n",
       " 'chromagram_mean_3',\n",
       " 'chromagram_mean_4',\n",
       " 'chromagram_mean_5',\n",
       " 'chromagram_mean_6',\n",
       " 'chromagram_mean_7',\n",
       " 'chromagram_mean_8',\n",
       " 'chromagram_mean_9',\n",
       " 'chromagram_mean_10',\n",
       " 'chromagram_mean_11',\n",
       " 'chromagram_mean_12',\n",
       " 'chromagram_std_1',\n",
       " 'chromagram_std_2',\n",
       " 'chromagram_std_3',\n",
       " 'chromagram_std_4',\n",
       " 'chromagram_std_5',\n",
       " 'chromagram_std_6',\n",
       " 'chromagram_std_7',\n",
       " 'chromagram_std_8',\n",
       " 'chromagram_std_9',\n",
       " 'chromagram_std_10',\n",
       " 'chromagram_std_11',\n",
       " 'chromagram_std_12',\n",
       " 'loudness_mean',\n",
       " 'loudness_std',\n",
       " 'energy_mean',\n",
       " 'energy_std',\n",
       " 'perceptual_sharp_mean',\n",
       " 'perceptual_sharp_std',\n",
       " 'spectral_slope_mean',\n",
       " 'spectral_slope_std']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create list of usable features\n",
    "mean_str = 'mean'\n",
    "std_str = 'std'\n",
    "num_mfcc = 13\n",
    "num_chroma = 12\n",
    "\n",
    "chroma_list = []\n",
    "mfcc_list = []\n",
    "features_list = [None] * len(features) * 2\n",
    "\n",
    "mfcc_idx = features.index('mfcc') * 2\n",
    "chroma_idx = features.index('chromagram') * 2\n",
    "\n",
    "\n",
    "for i in range(num_chroma):\n",
    "    chroma_str = f'chromagram_{mean_str}_{str(i+1)}'\n",
    "    chroma_list.append(chroma_str)\n",
    "\n",
    "for i in range(num_chroma):\n",
    "    chroma_str = f'chromagram_{std_str}_{str(i+1)}'\n",
    "    chroma_list.append(chroma_str)\n",
    "\n",
    "\n",
    "for i in range(num_mfcc):\n",
    "    mfcc_str = f'mfcc_{mean_str}_{str(i+1)}'\n",
    "    mfcc_list.append(mfcc_str)\n",
    "\n",
    "for i in range(num_mfcc):\n",
    "    mfcc_str = f'mfcc_{std_str}_{str(i+1)}'\n",
    "    mfcc_list.append(mfcc_str)\n",
    "\n",
    "mean_list = [f'{feature}_{mean_str}' for feature in features]\n",
    "std_list = [f'{feature}_{std_str}' for feature in features]\n",
    "\n",
    "features_list[::2] = mean_list\n",
    "features_list[1::2] = std_list\n",
    "\n",
    "features_list.pop(chroma_idx + 1)\n",
    "features_list.pop(chroma_idx)\n",
    "features_list[chroma_idx:chroma_idx] = chroma_list\n",
    "features_list.pop(mfcc_idx + 1)\n",
    "features_list.pop(mfcc_idx)\n",
    "features_list[mfcc_idx:mfcc_idx] = mfcc_list\n",
    "\n",
    "print(len(features_list))\n",
    "features_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rms_mean                 float32\n",
      "rms_std                  float32\n",
      "zerocross_mean           float32\n",
      "zerocross_std            float32\n",
      "rolloff_mean             float32\n",
      "                          ...   \n",
      "energy_std               float32\n",
      "perceptual_sharp_mean    float32\n",
      "perceptual_sharp_std     float32\n",
      "spectral_slope_mean      float32\n",
      "spectral_slope_std       float32\n",
      "Length: 74, dtype: object\n"
     ]
    }
   ],
   "source": [
    "# filter data to accomodate available features\n",
    "f_features_norm = features_norm.filter(features_list).astype(np.float32)\n",
    "print(f_features_norm.dtypes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train Deployment Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train Arousal Neural Net"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Arousal Neural Net\n",
    "\n",
    "aro_model = keras.Sequential([\n",
    "    keras.layers.Dense(50, activation='relu', input_shape=(74,)),\n",
    "    keras.layers.Dropout(0.2),\n",
    "    keras.layers.Dense(20, activation='relu'),\n",
    "    keras.layers.Dropout(0.2),\n",
    "    keras.layers.Dense(1, activation='linear')\n",
    "])\n",
    "\n",
    "aro_model.compile(\n",
    "    optimizer=keras.optimizers.RMSprop(),\n",
    "    loss=keras.losses.MeanSquaredError(),\n",
    "    metrics=[\n",
    "        MeanSquaredError(),\n",
    "        RSquare(y_shape=(1,))\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense (Dense)               (None, 50)                3750      \n",
      "                                                                 \n",
      " dropout (Dropout)           (None, 50)                0         \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 20)                1020      \n",
      "                                                                 \n",
      " dropout_1 (Dropout)         (None, 20)                0         \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 1)                 21        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 4,791\n",
      "Trainable params: 4,791\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "aro_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "38/38 [==============================] - 1s 1ms/step - loss: 0.3034 - mean_squared_error: 0.3034 - r_square: 0.0893\n",
      "Epoch 2/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.2079 - mean_squared_error: 0.2079 - r_square: 0.3760\n",
      "Epoch 3/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.1638 - mean_squared_error: 0.1638 - r_square: 0.5085\n",
      "Epoch 4/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.1314 - mean_squared_error: 0.1314 - r_square: 0.6056\n",
      "Epoch 5/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.1184 - mean_squared_error: 0.1184 - r_square: 0.6447\n",
      "Epoch 6/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.1084 - mean_squared_error: 0.1084 - r_square: 0.6746\n",
      "Epoch 7/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.1004 - mean_squared_error: 0.1004 - r_square: 0.6988\n",
      "Epoch 8/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.0915 - mean_squared_error: 0.0915 - r_square: 0.7253\n",
      "Epoch 9/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.0909 - mean_squared_error: 0.0909 - r_square: 0.7271\n",
      "Epoch 10/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.0843 - mean_squared_error: 0.0843 - r_square: 0.7470\n",
      "Epoch 11/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.0808 - mean_squared_error: 0.0808 - r_square: 0.7574\n",
      "Epoch 12/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.0815 - mean_squared_error: 0.0815 - r_square: 0.7555\n",
      "Epoch 13/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.0800 - mean_squared_error: 0.0800 - r_square: 0.7598\n",
      "Epoch 14/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.0754 - mean_squared_error: 0.0754 - r_square: 0.7736\n",
      "Epoch 15/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.0773 - mean_squared_error: 0.0773 - r_square: 0.7679\n",
      "Epoch 16/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.0748 - mean_squared_error: 0.0748 - r_square: 0.7755\n",
      "Epoch 17/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.0662 - mean_squared_error: 0.0662 - r_square: 0.8013\n",
      "Epoch 18/100\n",
      "38/38 [==============================] - 0s 960us/step - loss: 0.0690 - mean_squared_error: 0.0690 - r_square: 0.7930\n",
      "Epoch 19/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.0643 - mean_squared_error: 0.0643 - r_square: 0.8070\n",
      "Epoch 20/100\n",
      "38/38 [==============================] - 0s 998us/step - loss: 0.0680 - mean_squared_error: 0.0680 - r_square: 0.7959\n",
      "Epoch 21/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.0649 - mean_squared_error: 0.0649 - r_square: 0.8052\n",
      "Epoch 22/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.0647 - mean_squared_error: 0.0647 - r_square: 0.8058\n",
      "Epoch 23/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.0615 - mean_squared_error: 0.0615 - r_square: 0.8156\n",
      "Epoch 24/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.0623 - mean_squared_error: 0.0623 - r_square: 0.8131\n",
      "Epoch 25/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.0633 - mean_squared_error: 0.0633 - r_square: 0.8101\n",
      "Epoch 26/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.0602 - mean_squared_error: 0.0602 - r_square: 0.8194\n",
      "Epoch 27/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.0601 - mean_squared_error: 0.0601 - r_square: 0.8196\n",
      "Epoch 28/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.0605 - mean_squared_error: 0.0605 - r_square: 0.8184\n",
      "Epoch 29/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.0562 - mean_squared_error: 0.0562 - r_square: 0.8312\n",
      "Epoch 30/100\n",
      "38/38 [==============================] - 0s 957us/step - loss: 0.0608 - mean_squared_error: 0.0608 - r_square: 0.8176\n",
      "Epoch 31/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.0604 - mean_squared_error: 0.0604 - r_square: 0.8187\n",
      "Epoch 32/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.0536 - mean_squared_error: 0.0536 - r_square: 0.8390\n",
      "Epoch 33/100\n",
      "38/38 [==============================] - 0s 994us/step - loss: 0.0577 - mean_squared_error: 0.0577 - r_square: 0.8267\n",
      "Epoch 34/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.0575 - mean_squared_error: 0.0575 - r_square: 0.8276\n",
      "Epoch 35/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.0563 - mean_squared_error: 0.0563 - r_square: 0.8310\n",
      "Epoch 36/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.0559 - mean_squared_error: 0.0559 - r_square: 0.8321\n",
      "Epoch 37/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.0554 - mean_squared_error: 0.0554 - r_square: 0.8339\n",
      "Epoch 38/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.0587 - mean_squared_error: 0.0587 - r_square: 0.8238\n",
      "Epoch 39/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.0549 - mean_squared_error: 0.0549 - r_square: 0.8353\n",
      "Epoch 40/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.0550 - mean_squared_error: 0.0550 - r_square: 0.8351\n",
      "Epoch 41/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.0527 - mean_squared_error: 0.0527 - r_square: 0.8417\n",
      "Epoch 42/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.0491 - mean_squared_error: 0.0491 - r_square: 0.8526\n",
      "Epoch 43/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.0518 - mean_squared_error: 0.0518 - r_square: 0.8446\n",
      "Epoch 44/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.0540 - mean_squared_error: 0.0540 - r_square: 0.8379\n",
      "Epoch 45/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.0520 - mean_squared_error: 0.0520 - r_square: 0.8439\n",
      "Epoch 46/100\n",
      "38/38 [==============================] - 0s 952us/step - loss: 0.0537 - mean_squared_error: 0.0537 - r_square: 0.8389\n",
      "Epoch 47/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.0500 - mean_squared_error: 0.0500 - r_square: 0.8499\n",
      "Epoch 48/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.0485 - mean_squared_error: 0.0485 - r_square: 0.8544\n",
      "Epoch 49/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.0513 - mean_squared_error: 0.0513 - r_square: 0.8462\n",
      "Epoch 50/100\n",
      "38/38 [==============================] - 0s 979us/step - loss: 0.0502 - mean_squared_error: 0.0502 - r_square: 0.8493\n",
      "Epoch 51/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.0542 - mean_squared_error: 0.0542 - r_square: 0.8373\n",
      "Epoch 52/100\n",
      "38/38 [==============================] - 0s 985us/step - loss: 0.0506 - mean_squared_error: 0.0506 - r_square: 0.8481\n",
      "Epoch 53/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.0513 - mean_squared_error: 0.0513 - r_square: 0.8461\n",
      "Epoch 54/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.0519 - mean_squared_error: 0.0519 - r_square: 0.8442\n",
      "Epoch 55/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.0480 - mean_squared_error: 0.0480 - r_square: 0.8561\n",
      "Epoch 56/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.0529 - mean_squared_error: 0.0529 - r_square: 0.8412\n",
      "Epoch 57/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.0479 - mean_squared_error: 0.0479 - r_square: 0.8563\n",
      "Epoch 58/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.0467 - mean_squared_error: 0.0467 - r_square: 0.8598\n",
      "Epoch 59/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.0495 - mean_squared_error: 0.0495 - r_square: 0.8514\n",
      "Epoch 60/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.0475 - mean_squared_error: 0.0475 - r_square: 0.8574\n",
      "Epoch 61/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.0468 - mean_squared_error: 0.0468 - r_square: 0.8596\n",
      "Epoch 62/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.0491 - mean_squared_error: 0.0491 - r_square: 0.8526\n",
      "Epoch 63/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.0463 - mean_squared_error: 0.0463 - r_square: 0.8609\n",
      "Epoch 64/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.0459 - mean_squared_error: 0.0459 - r_square: 0.8624\n",
      "Epoch 65/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.0452 - mean_squared_error: 0.0452 - r_square: 0.8644\n",
      "Epoch 66/100\n",
      "38/38 [==============================] - 0s 942us/step - loss: 0.0463 - mean_squared_error: 0.0463 - r_square: 0.8610\n",
      "Epoch 67/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.0473 - mean_squared_error: 0.0473 - r_square: 0.8581\n",
      "Epoch 68/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.0502 - mean_squared_error: 0.0502 - r_square: 0.8493\n",
      "Epoch 69/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.0442 - mean_squared_error: 0.0442 - r_square: 0.8672\n",
      "Epoch 70/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.0428 - mean_squared_error: 0.0428 - r_square: 0.8717\n",
      "Epoch 71/100\n",
      "38/38 [==============================] - 0s 981us/step - loss: 0.0489 - mean_squared_error: 0.0489 - r_square: 0.8531\n",
      "Epoch 72/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.0464 - mean_squared_error: 0.0464 - r_square: 0.8606\n",
      "Epoch 73/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.0496 - mean_squared_error: 0.0496 - r_square: 0.8513\n",
      "Epoch 74/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.0458 - mean_squared_error: 0.0458 - r_square: 0.8627\n",
      "Epoch 75/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.0451 - mean_squared_error: 0.0451 - r_square: 0.8646\n",
      "Epoch 76/100\n",
      "38/38 [==============================] - 0s 949us/step - loss: 0.0467 - mean_squared_error: 0.0467 - r_square: 0.8598\n",
      "Epoch 77/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.0471 - mean_squared_error: 0.0471 - r_square: 0.8586\n",
      "Epoch 78/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.0460 - mean_squared_error: 0.0460 - r_square: 0.8620\n",
      "Epoch 79/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.0449 - mean_squared_error: 0.0449 - r_square: 0.8651\n",
      "Epoch 80/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.0470 - mean_squared_error: 0.0470 - r_square: 0.8590\n",
      "Epoch 81/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.0451 - mean_squared_error: 0.0451 - r_square: 0.8647\n",
      "Epoch 82/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.0457 - mean_squared_error: 0.0457 - r_square: 0.8629\n",
      "Epoch 83/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.0436 - mean_squared_error: 0.0436 - r_square: 0.8690\n",
      "Epoch 84/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.0450 - mean_squared_error: 0.0450 - r_square: 0.8649\n",
      "Epoch 85/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.0431 - mean_squared_error: 0.0431 - r_square: 0.8707\n",
      "Epoch 86/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.0456 - mean_squared_error: 0.0456 - r_square: 0.8630\n",
      "Epoch 87/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.0430 - mean_squared_error: 0.0430 - r_square: 0.8710\n",
      "Epoch 88/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.0416 - mean_squared_error: 0.0416 - r_square: 0.8750\n",
      "Epoch 89/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.0446 - mean_squared_error: 0.0446 - r_square: 0.8662\n",
      "Epoch 90/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.0454 - mean_squared_error: 0.0454 - r_square: 0.8638\n",
      "Epoch 91/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.0447 - mean_squared_error: 0.0447 - r_square: 0.8660\n",
      "Epoch 92/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.0423 - mean_squared_error: 0.0423 - r_square: 0.8731\n",
      "Epoch 93/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.0445 - mean_squared_error: 0.0445 - r_square: 0.8665\n",
      "Epoch 94/100\n",
      "38/38 [==============================] - 0s 999us/step - loss: 0.0422 - mean_squared_error: 0.0422 - r_square: 0.8734\n",
      "Epoch 95/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.0445 - mean_squared_error: 0.0445 - r_square: 0.8665\n",
      "Epoch 96/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.0417 - mean_squared_error: 0.0417 - r_square: 0.8750\n",
      "Epoch 97/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.0400 - mean_squared_error: 0.0400 - r_square: 0.8800\n",
      "Epoch 98/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.0443 - mean_squared_error: 0.0443 - r_square: 0.8670\n",
      "Epoch 99/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.0419 - mean_squared_error: 0.0419 - r_square: 0.8742\n",
      "Epoch 100/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.0438 - mean_squared_error: 0.0438 - r_square: 0.8684\n"
     ]
    }
   ],
   "source": [
    "aro_history = aro_model.fit(f_features_norm, aro_y, epochs=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Arousal Model Training History\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mse = aro_history.history['mean_squared_error']\n",
    "r2 = aro_history.history['r_square']\n",
    "\n",
    "epochs = range(len(mse)) # Get number of epochs\n",
    "\n",
    "#------------------------------------------------\n",
    "# Plot training MSE per epoch\n",
    "#------------------------------------------------\n",
    "plt.figure(figsize=(20,10))\n",
    "plt.plot  (epochs, mse, label='MSE')\n",
    "\n",
    "#------------------------------------------------\n",
    "# Plot training r2 per epoch\n",
    "#------------------------------------------------\n",
    "plt.plot  (epochs, r2, label='r_squated')\n",
    "plt.xlabel('Epochs')\n",
    "plt.legend()\n",
    "plt.title ('Training History')\n",
    "\n",
    "print('Arousal Model Training History')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train Valence Neural Net"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Valence Neural Net\n",
    "\n",
    "val_model = keras.Sequential([\n",
    "    keras.layers.Dense(50, activation='relu', input_shape=(74,)),\n",
    "    keras.layers.Dropout(0.2),\n",
    "    keras.layers.Dense(20, activation='relu'),\n",
    "    keras.layers.Dropout(0.2),\n",
    "    keras.layers.Dense(1, activation='linear')\n",
    "])\n",
    "\n",
    "val_model.compile(\n",
    "    optimizer=keras.optimizers.RMSprop(),\n",
    "    loss=keras.losses.MeanSquaredError(),\n",
    "    metrics=[\n",
    "        MeanSquaredError(),\n",
    "        RSquare(y_shape=(1,))\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_3\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense_9 (Dense)             (None, 50)                3750      \n",
      "                                                                 \n",
      " dropout_6 (Dropout)         (None, 50)                0         \n",
      "                                                                 \n",
      " dense_10 (Dense)            (None, 20)                1020      \n",
      "                                                                 \n",
      " dropout_7 (Dropout)         (None, 20)                0         \n",
      "                                                                 \n",
      " dense_11 (Dense)            (None, 1)                 21        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 4,791\n",
      "Trainable params: 4,791\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "val_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.3106 - mean_squared_error: 0.3106 - r_square: 0.0682\n",
      "Epoch 2/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.2484 - mean_squared_error: 0.2484 - r_square: 0.2547\n",
      "Epoch 3/100\n",
      "38/38 [==============================] - 0s 2ms/step - loss: 0.2104 - mean_squared_error: 0.2104 - r_square: 0.3689\n",
      "Epoch 4/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.1892 - mean_squared_error: 0.1892 - r_square: 0.4324\n",
      "Epoch 5/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.1770 - mean_squared_error: 0.1770 - r_square: 0.4690\n",
      "Epoch 6/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.1761 - mean_squared_error: 0.1761 - r_square: 0.4716\n",
      "Epoch 7/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.1691 - mean_squared_error: 0.1691 - r_square: 0.4927\n",
      "Epoch 8/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.1576 - mean_squared_error: 0.1576 - r_square: 0.5272\n",
      "Epoch 9/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.1528 - mean_squared_error: 0.1528 - r_square: 0.5417\n",
      "Epoch 10/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.1422 - mean_squared_error: 0.1422 - r_square: 0.5733\n",
      "Epoch 11/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.1518 - mean_squared_error: 0.1518 - r_square: 0.5447\n",
      "Epoch 12/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.1341 - mean_squared_error: 0.1341 - r_square: 0.5977\n",
      "Epoch 13/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.1405 - mean_squared_error: 0.1405 - r_square: 0.5784\n",
      "Epoch 14/100\n",
      "38/38 [==============================] - 0s 999us/step - loss: 0.1330 - mean_squared_error: 0.1330 - r_square: 0.6010\n",
      "Epoch 15/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.1407 - mean_squared_error: 0.1407 - r_square: 0.5780\n",
      "Epoch 16/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.1332 - mean_squared_error: 0.1332 - r_square: 0.6003\n",
      "Epoch 17/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.1247 - mean_squared_error: 0.1247 - r_square: 0.6260\n",
      "Epoch 18/100\n",
      "38/38 [==============================] - 0s 997us/step - loss: 0.1371 - mean_squared_error: 0.1371 - r_square: 0.5888\n",
      "Epoch 19/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.1317 - mean_squared_error: 0.1317 - r_square: 0.6050\n",
      "Epoch 20/100\n",
      "38/38 [==============================] - 0s 997us/step - loss: 0.1210 - mean_squared_error: 0.1210 - r_square: 0.6369\n",
      "Epoch 21/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.1283 - mean_squared_error: 0.1283 - r_square: 0.6152\n",
      "Epoch 22/100\n",
      "38/38 [==============================] - 0s 997us/step - loss: 0.1278 - mean_squared_error: 0.1278 - r_square: 0.6167\n",
      "Epoch 23/100\n",
      "38/38 [==============================] - 0s 2ms/step - loss: 0.1222 - mean_squared_error: 0.1222 - r_square: 0.6335\n",
      "Epoch 24/100\n",
      "38/38 [==============================] - 0s 2ms/step - loss: 0.1231 - mean_squared_error: 0.1231 - r_square: 0.6306\n",
      "Epoch 25/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.1208 - mean_squared_error: 0.1208 - r_square: 0.6377\n",
      "Epoch 26/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.1206 - mean_squared_error: 0.1206 - r_square: 0.6383\n",
      "Epoch 27/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.1206 - mean_squared_error: 0.1206 - r_square: 0.6383\n",
      "Epoch 28/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.1184 - mean_squared_error: 0.1184 - r_square: 0.6448\n",
      "Epoch 29/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.1165 - mean_squared_error: 0.1165 - r_square: 0.6506\n",
      "Epoch 30/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.1153 - mean_squared_error: 0.1153 - r_square: 0.6540\n",
      "Epoch 31/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.1129 - mean_squared_error: 0.1129 - r_square: 0.6614\n",
      "Epoch 32/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.1148 - mean_squared_error: 0.1148 - r_square: 0.6555\n",
      "Epoch 33/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.1132 - mean_squared_error: 0.1132 - r_square: 0.6603\n",
      "Epoch 34/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.1144 - mean_squared_error: 0.1144 - r_square: 0.6569\n",
      "Epoch 35/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.1124 - mean_squared_error: 0.1124 - r_square: 0.6628\n",
      "Epoch 36/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.1074 - mean_squared_error: 0.1074 - r_square: 0.6778\n",
      "Epoch 37/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.1113 - mean_squared_error: 0.1113 - r_square: 0.6661\n",
      "Epoch 38/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.1090 - mean_squared_error: 0.1090 - r_square: 0.6729\n",
      "Epoch 39/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.1114 - mean_squared_error: 0.1114 - r_square: 0.6659\n",
      "Epoch 40/100\n",
      "38/38 [==============================] - 0s 983us/step - loss: 0.1098 - mean_squared_error: 0.1098 - r_square: 0.6705\n",
      "Epoch 41/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.1109 - mean_squared_error: 0.1109 - r_square: 0.6674\n",
      "Epoch 42/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.1071 - mean_squared_error: 0.1071 - r_square: 0.6786\n",
      "Epoch 43/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.1073 - mean_squared_error: 0.1073 - r_square: 0.6780\n",
      "Epoch 44/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.1036 - mean_squared_error: 0.1036 - r_square: 0.6892\n",
      "Epoch 45/100\n",
      "38/38 [==============================] - 0s 916us/step - loss: 0.1068 - mean_squared_error: 0.1068 - r_square: 0.6797\n",
      "Epoch 46/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.1092 - mean_squared_error: 0.1092 - r_square: 0.6725\n",
      "Epoch 47/100\n",
      "38/38 [==============================] - 0s 997us/step - loss: 0.1084 - mean_squared_error: 0.1084 - r_square: 0.6749\n",
      "Epoch 48/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.1056 - mean_squared_error: 0.1056 - r_square: 0.6832\n",
      "Epoch 49/100\n",
      "38/38 [==============================] - 0s 922us/step - loss: 0.1050 - mean_squared_error: 0.1050 - r_square: 0.6849\n",
      "Epoch 50/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.1093 - mean_squared_error: 0.1093 - r_square: 0.6722\n",
      "Epoch 51/100\n",
      "38/38 [==============================] - 0s 942us/step - loss: 0.1052 - mean_squared_error: 0.1052 - r_square: 0.6845\n",
      "Epoch 52/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.1063 - mean_squared_error: 0.1063 - r_square: 0.6811\n",
      "Epoch 53/100\n",
      "38/38 [==============================] - 0s 943us/step - loss: 0.1013 - mean_squared_error: 0.1013 - r_square: 0.6960\n",
      "Epoch 54/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.0978 - mean_squared_error: 0.0978 - r_square: 0.7066\n",
      "Epoch 55/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.1035 - mean_squared_error: 0.1035 - r_square: 0.6896\n",
      "Epoch 56/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.1015 - mean_squared_error: 0.1015 - r_square: 0.6955\n",
      "Epoch 57/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.1044 - mean_squared_error: 0.1044 - r_square: 0.6867\n",
      "Epoch 58/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.1012 - mean_squared_error: 0.1012 - r_square: 0.6964\n",
      "Epoch 59/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.1020 - mean_squared_error: 0.1020 - r_square: 0.6941\n",
      "Epoch 60/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.1003 - mean_squared_error: 0.1003 - r_square: 0.6990\n",
      "Epoch 61/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.1037 - mean_squared_error: 0.1037 - r_square: 0.6890\n",
      "Epoch 62/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.1015 - mean_squared_error: 0.1015 - r_square: 0.6954\n",
      "Epoch 63/100\n",
      "38/38 [==============================] - 0s 970us/step - loss: 0.1014 - mean_squared_error: 0.1014 - r_square: 0.6957\n",
      "Epoch 64/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.0983 - mean_squared_error: 0.0983 - r_square: 0.7050\n",
      "Epoch 65/100\n",
      "38/38 [==============================] - 0s 971us/step - loss: 0.0977 - mean_squared_error: 0.0977 - r_square: 0.7068\n",
      "Epoch 66/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.0994 - mean_squared_error: 0.0994 - r_square: 0.7018\n",
      "Epoch 67/100\n",
      "38/38 [==============================] - 0s 944us/step - loss: 0.1005 - mean_squared_error: 0.1005 - r_square: 0.6986\n",
      "Epoch 68/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.1020 - mean_squared_error: 0.1020 - r_square: 0.6939\n",
      "Epoch 69/100\n",
      "38/38 [==============================] - 0s 943us/step - loss: 0.0955 - mean_squared_error: 0.0955 - r_square: 0.7136\n",
      "Epoch 70/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.0999 - mean_squared_error: 0.0999 - r_square: 0.7004\n",
      "Epoch 71/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.0975 - mean_squared_error: 0.0975 - r_square: 0.7075\n",
      "Epoch 72/100\n",
      "38/38 [==============================] - 0s 960us/step - loss: 0.0972 - mean_squared_error: 0.0972 - r_square: 0.7085\n",
      "Epoch 73/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.0952 - mean_squared_error: 0.0952 - r_square: 0.7144\n",
      "Epoch 74/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.0990 - mean_squared_error: 0.0990 - r_square: 0.7030\n",
      "Epoch 75/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.0947 - mean_squared_error: 0.0947 - r_square: 0.7160\n",
      "Epoch 76/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.0975 - mean_squared_error: 0.0975 - r_square: 0.7074\n",
      "Epoch 77/100\n",
      "38/38 [==============================] - 0s 949us/step - loss: 0.0955 - mean_squared_error: 0.0955 - r_square: 0.7136\n",
      "Epoch 78/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.1002 - mean_squared_error: 0.1002 - r_square: 0.6994\n",
      "Epoch 79/100\n",
      "38/38 [==============================] - 0s 944us/step - loss: 0.0972 - mean_squared_error: 0.0972 - r_square: 0.7085\n",
      "Epoch 80/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.0987 - mean_squared_error: 0.0987 - r_square: 0.7039\n",
      "Epoch 81/100\n",
      "38/38 [==============================] - 0s 909us/step - loss: 0.0975 - mean_squared_error: 0.0975 - r_square: 0.7076\n",
      "Epoch 82/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.0953 - mean_squared_error: 0.0953 - r_square: 0.7140\n",
      "Epoch 83/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.0955 - mean_squared_error: 0.0955 - r_square: 0.7136\n",
      "Epoch 84/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.0951 - mean_squared_error: 0.0951 - r_square: 0.7148\n",
      "Epoch 85/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.0946 - mean_squared_error: 0.0946 - r_square: 0.7163\n",
      "Epoch 86/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.0980 - mean_squared_error: 0.0980 - r_square: 0.7059\n",
      "Epoch 87/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.0932 - mean_squared_error: 0.0932 - r_square: 0.7203\n",
      "Epoch 88/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.0961 - mean_squared_error: 0.0961 - r_square: 0.7116\n",
      "Epoch 89/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.0955 - mean_squared_error: 0.0955 - r_square: 0.7135\n",
      "Epoch 90/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.0904 - mean_squared_error: 0.0904 - r_square: 0.7288\n",
      "Epoch 91/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.0932 - mean_squared_error: 0.0932 - r_square: 0.7205\n",
      "Epoch 92/100\n",
      "38/38 [==============================] - 0s 985us/step - loss: 0.0958 - mean_squared_error: 0.0958 - r_square: 0.7126\n",
      "Epoch 93/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.0930 - mean_squared_error: 0.0930 - r_square: 0.7210\n",
      "Epoch 94/100\n",
      "38/38 [==============================] - 0s 932us/step - loss: 0.0935 - mean_squared_error: 0.0935 - r_square: 0.7195\n",
      "Epoch 95/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.0929 - mean_squared_error: 0.0929 - r_square: 0.7214\n",
      "Epoch 96/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.0900 - mean_squared_error: 0.0900 - r_square: 0.7301\n",
      "Epoch 97/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.0939 - mean_squared_error: 0.0939 - r_square: 0.7184\n",
      "Epoch 98/100\n",
      "38/38 [==============================] - 0s 995us/step - loss: 0.0899 - mean_squared_error: 0.0899 - r_square: 0.7302\n",
      "Epoch 99/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.0951 - mean_squared_error: 0.0951 - r_square: 0.7148\n",
      "Epoch 100/100\n",
      "38/38 [==============================] - 0s 1ms/step - loss: 0.0906 - mean_squared_error: 0.0906 - r_square: 0.7281\n"
     ]
    }
   ],
   "source": [
    "val_history = val_model.fit(f_features_norm, val_y, epochs=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Valance Model Training History\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mse = val_history.history['mean_squared_error']\n",
    "r2 = val_history.history['r_square']\n",
    "\n",
    "epochs = range(len(mse)) # Get number of epochs\n",
    "\n",
    "#------------------------------------------------\n",
    "# Plot training MSE per epoch\n",
    "#------------------------------------------------\n",
    "plt.figure(figsize=(20,10))\n",
    "plt.plot  (epochs, mse, label='MSE')\n",
    "\n",
    "#------------------------------------------------\n",
    "# Plot training r2 per epoch\n",
    "#------------------------------------------------\n",
    "plt.plot  (epochs, r2, label='r_squated')\n",
    "plt.xlabel('Epochs')\n",
    "plt.legend()\n",
    "plt.title ('Training History')\n",
    "\n",
    "print('Valence Model Training History')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Metrics\n",
    "|                    | Arousal | Valence |\n",
    "| :----------------: | :-----: | :-----: |\n",
    "|      R Squared     | 0.8684  | 0.7281  |\n",
    "| Mean Squared Error | 0.0438  | 0.0906  |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Save Models as Keras HDF5 file"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save Arousal Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "tf.keras.models.save_model(aro_model, './_models/arousal/aro_model.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save Valence Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "tf.keras.models.save_model(val_model, './_models/valence/val_model.h5')"
   ]
  }
 ],
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