Sound_classification_LSTM.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "lstm (LSTM)                  (None, 108, 100)          45600     \n",
      "_________________________________________________________________\n",
      "lstm_1 (LSTM)                (None, 108, 100)          80400     \n",
      "_________________________________________________________________\n",
      "lstm_2 (LSTM)                (None, 108, 100)          80400     \n",
      "_________________________________________________________________\n",
      "lstm_3 (LSTM)                (None, 100)               80400     \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 112)               11312     \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 2)                 226       \n",
      "=================================================================\n",
      "Total params: 298,338\n",
      "Trainable params: 298,338\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Train on 4909 samples, validate on 1228 samples\n",
      "Epoch 1/30\n",
      "4909/4909 [==============================] - 78s 16ms/sample - loss: 0.6002 - accuracy: 0.6796 - val_loss: 0.6031 - val_accuracy: 0.6735\n",
      "Epoch 2/30\n",
      "4909/4909 [==============================] - 54s 11ms/sample - loss: 0.5460 - accuracy: 0.7232 - val_loss: 0.5770 - val_accuracy: 0.6995\n",
      "Epoch 3/30\n",
      "4909/4909 [==============================] - 54s 11ms/sample - loss: 0.5192 - accuracy: 0.7431 - val_loss: 0.5834 - val_accuracy: 0.6824\n",
      "Epoch 4/30\n",
      "4909/4909 [==============================] - 60s 12ms/sample - loss: 0.4982 - accuracy: 0.7556 - val_loss: 0.5911 - val_accuracy: 0.7158\n",
      "Epoch 5/30\n",
      "4909/4909 [==============================] - 61s 12ms/sample - loss: 0.4814 - accuracy: 0.7700 - val_loss: 0.5694 - val_accuracy: 0.6987\n",
      "Epoch 6/30\n",
      "4909/4909 [==============================] - 60s 12ms/sample - loss: 0.4605 - accuracy: 0.7806 - val_loss: 0.5660 - val_accuracy: 0.7052\n",
      "Epoch 7/30\n",
      "4909/4909 [==============================] - 66s 13ms/sample - loss: 0.4429 - accuracy: 0.7883 - val_loss: 0.5567 - val_accuracy: 0.7191\n",
      "Epoch 8/30\n",
      "4909/4909 [==============================] - 54s 11ms/sample - loss: 0.4254 - accuracy: 0.8032 - val_loss: 0.5885 - val_accuracy: 0.7036\n",
      "Epoch 9/30\n",
      "4909/4909 [==============================] - 54s 11ms/sample - loss: 0.4141 - accuracy: 0.8044 - val_loss: 0.5931 - val_accuracy: 0.7077\n",
      "Epoch 10/30\n",
      "4909/4909 [==============================] - 53s 11ms/sample - loss: 0.3952 - accuracy: 0.8158 - val_loss: 0.6153 - val_accuracy: 0.7207\n",
      "Epoch 11/30\n",
      "4909/4909 [==============================] - 57s 12ms/sample - loss: 0.3790 - accuracy: 0.8246 - val_loss: 0.6187 - val_accuracy: 0.7134\n",
      "Epoch 12/30\n",
      "4909/4909 [==============================] - 59s 12ms/sample - loss: 0.3683 - accuracy: 0.8301 - val_loss: 0.6572 - val_accuracy: 0.7207\n",
      "Epoch 13/30\n",
      "4909/4909 [==============================] - 56s 12ms/sample - loss: 0.3583 - accuracy: 0.8313 - val_loss: 0.6842 - val_accuracy: 0.7150\n",
      "Epoch 14/30\n",
      "4909/4909 [==============================] - 52s 11ms/sample - loss: 0.3431 - accuracy: 0.8438 - val_loss: 0.6998 - val_accuracy: 0.7223\n",
      "Epoch 15/30\n",
      "4909/4909 [==============================] - 52s 11ms/sample - loss: 0.3264 - accuracy: 0.8482 - val_loss: 0.7650 - val_accuracy: 0.7052\n",
      "Epoch 16/30\n",
      "4909/4909 [==============================] - 57s 12ms/sample - loss: 0.3148 - accuracy: 0.8535 - val_loss: 0.7774 - val_accuracy: 0.7085\n",
      "Epoch 17/30\n",
      "4909/4909 [==============================] - 55s 11ms/sample - loss: 0.2938 - accuracy: 0.8647 - val_loss: 0.8942 - val_accuracy: 0.7085\n",
      "Epoch 18/30\n",
      "4909/4909 [==============================] - 52s 11ms/sample - loss: 0.2875 - accuracy: 0.8645 - val_loss: 0.8478 - val_accuracy: 0.7085\n",
      "Epoch 19/30\n",
      "4909/4909 [==============================] - 55s 11ms/sample - loss: 0.2759 - accuracy: 0.8739 - val_loss: 0.9211 - val_accuracy: 0.7077\n",
      "Epoch 20/30\n",
      "4909/4909 [==============================] - 58s 12ms/sample - loss: 0.2654 - accuracy: 0.8770 - val_loss: 0.9030 - val_accuracy: 0.7101\n",
      "Epoch 21/30\n",
      "4909/4909 [==============================] - 67s 14ms/sample - loss: 0.2488 - accuracy: 0.8831 - val_loss: 1.0637 - val_accuracy: 0.7093\n",
      "Epoch 22/30\n",
      "4909/4909 [==============================] - 71s 15ms/sample - loss: 0.2535 - accuracy: 0.8792 - val_loss: 1.0499 - val_accuracy: 0.6922\n",
      "Epoch 23/30\n",
      "4909/4909 [==============================] - 52s 11ms/sample - loss: 0.2396 - accuracy: 0.8869 - val_loss: 1.1482 - val_accuracy: 0.6873\n",
      "Epoch 24/30\n",
      "4909/4909 [==============================] - 74s 15ms/sample - loss: 0.2252 - accuracy: 0.8914 - val_loss: 1.2360 - val_accuracy: 0.6881\n",
      "Epoch 25/30\n",
      "4909/4909 [==============================] - 63s 13ms/sample - loss: 0.2205 - accuracy: 0.8947 - val_loss: 1.1138 - val_accuracy: 0.7068\n",
      "Epoch 26/30\n",
      "4909/4909 [==============================] - 64s 13ms/sample - loss: 0.2173 - accuracy: 0.8988 - val_loss: 1.2112 - val_accuracy: 0.6995\n",
      "Epoch 27/30\n",
      "4909/4909 [==============================] - 74s 15ms/sample - loss: 0.2052 - accuracy: 0.9026 - val_loss: 1.1787 - val_accuracy: 0.7003\n",
      "Epoch 28/30\n",
      "4909/4909 [==============================] - 71s 14ms/sample - loss: 0.2042 - accuracy: 0.9000 - val_loss: 1.1681 - val_accuracy: 0.6946\n",
      "Epoch 29/30\n",
      "4909/4909 [==============================] - 60s 12ms/sample - loss: 0.2113 - accuracy: 0.9043 - val_loss: 1.1558 - val_accuracy: 0.7085\n",
      "Epoch 30/30\n",
      "4909/4909 [==============================] - 63s 13ms/sample - loss: 0.2017 - accuracy: 0.9051 - val_loss: 1.3455 - val_accuracy: 0.6979\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2046/2046 - 8s - loss: 1.2786 - accuracy: 0.7243\n",
      "\n",
      "Test accuracy: 0.7243402\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "Error when checking input: expected lstm_input to have 3 dimensions, but got array with shape (108, 13)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-1-0947b5a451ee>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m    157\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    158\u001b[0m     \u001b[1;31m# predict sample\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 159\u001b[1;33m     \u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX_to_predict\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_to_predict\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    160\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-1-0947b5a451ee>\u001b[0m in \u001b[0;36mpredict\u001b[1;34m(model, X, y)\u001b[0m\n\u001b[0;32m    115\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    116\u001b[0m     \u001b[1;31m# perform prediction\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 117\u001b[1;33m     \u001b[0mprediction\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    118\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    119\u001b[0m     \u001b[1;31m# get index with max value\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\tensorflow_env\\lib\\site-packages\\tensorflow_core\\python\\keras\\engine\\training.py\u001b[0m in \u001b[0;36mpredict\u001b[1;34m(self, x, batch_size, verbose, steps, callbacks, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[0;32m   1011\u001b[0m         \u001b[0mmax_queue_size\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmax_queue_size\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1012\u001b[0m         \u001b[0mworkers\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mworkers\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1013\u001b[1;33m         use_multiprocessing=use_multiprocessing)\n\u001b[0m\u001b[0;32m   1014\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1015\u001b[0m   \u001b[1;32mdef\u001b[0m \u001b[0mreset_metrics\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\tensorflow_env\\lib\\site-packages\\tensorflow_core\\python\\keras\\engine\\training_v2.py\u001b[0m in \u001b[0;36mpredict\u001b[1;34m(self, model, x, batch_size, verbose, steps, callbacks, max_queue_size, workers, use_multiprocessing, **kwargs)\u001b[0m\n\u001b[0;32m    496\u001b[0m         \u001b[0mmodel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mModeKeys\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mPREDICT\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mverbose\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    497\u001b[0m         \u001b[0msteps\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msteps\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmax_queue_size\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmax_queue_size\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 498\u001b[1;33m         workers=workers, use_multiprocessing=use_multiprocessing, **kwargs)\n\u001b[0m\u001b[0;32m    499\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    500\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\tensorflow_env\\lib\\site-packages\\tensorflow_core\\python\\keras\\engine\\training_v2.py\u001b[0m in \u001b[0;36m_model_iteration\u001b[1;34m(self, model, mode, x, y, batch_size, verbose, sample_weight, steps, callbacks, max_queue_size, workers, use_multiprocessing, **kwargs)\u001b[0m\n\u001b[0;32m    424\u001b[0m           \u001b[0mmax_queue_size\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmax_queue_size\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    425\u001b[0m           \u001b[0mworkers\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mworkers\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 426\u001b[1;33m           use_multiprocessing=use_multiprocessing)\n\u001b[0m\u001b[0;32m    427\u001b[0m       \u001b[0mtotal_samples\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_get_total_number_of_samples\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0madapter\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    428\u001b[0m       \u001b[0muse_sample\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtotal_samples\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\tensorflow_env\\lib\\site-packages\\tensorflow_core\\python\\keras\\engine\\training_v2.py\u001b[0m in \u001b[0;36m_process_inputs\u001b[1;34m(model, mode, x, y, batch_size, epochs, sample_weights, class_weights, shuffle, steps, distribution_strategy, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[0;32m    644\u001b[0m     \u001b[0mstandardize_function\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    645\u001b[0m     x, y, sample_weights = standardize(\n\u001b[1;32m--> 646\u001b[1;33m         x, y, sample_weight=sample_weights)\n\u001b[0m\u001b[0;32m    647\u001b[0m   \u001b[1;32melif\u001b[0m \u001b[0madapter_cls\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mdata_adapter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mListsOfScalarsDataAdapter\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    648\u001b[0m     \u001b[0mstandardize_function\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mstandardize\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\tensorflow_env\\lib\\site-packages\\tensorflow_core\\python\\keras\\engine\\training.py\u001b[0m in \u001b[0;36m_standardize_user_data\u001b[1;34m(self, x, y, sample_weight, class_weight, batch_size, check_steps, steps_name, steps, validation_split, shuffle, extract_tensors_from_dataset)\u001b[0m\n\u001b[0;32m   2381\u001b[0m         \u001b[0mis_dataset\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mis_dataset\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2382\u001b[0m         \u001b[0mclass_weight\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mclass_weight\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2383\u001b[1;33m         batch_size=batch_size)\n\u001b[0m\u001b[0;32m   2384\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2385\u001b[0m   def _standardize_tensors(self, x, y, sample_weight, run_eagerly, dict_inputs,\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\tensorflow_env\\lib\\site-packages\\tensorflow_core\\python\\keras\\engine\\training.py\u001b[0m in \u001b[0;36m_standardize_tensors\u001b[1;34m(self, x, y, sample_weight, run_eagerly, dict_inputs, is_dataset, class_weight, batch_size)\u001b[0m\n\u001b[0;32m   2408\u001b[0m           \u001b[0mfeed_input_shapes\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2409\u001b[0m           \u001b[0mcheck_batch_axis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m  \u001b[1;31m# Don't enforce the batch size.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2410\u001b[1;33m           exception_prefix='input')\n\u001b[0m\u001b[0;32m   2411\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2412\u001b[0m     \u001b[1;31m# Get typespecs for the input data and sanitize it if necessary.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\tensorflow_env\\lib\\site-packages\\tensorflow_core\\python\\keras\\engine\\training_utils.py\u001b[0m in \u001b[0;36mstandardize_input_data\u001b[1;34m(data, names, shapes, check_batch_axis, exception_prefix)\u001b[0m\n\u001b[0;32m    571\u001b[0m                            \u001b[1;34m': expected '\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mnames\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;34m' to have '\u001b[0m \u001b[1;33m+\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    572\u001b[0m                            \u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;34m' dimensions, but got array '\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 573\u001b[1;33m                            'with shape ' + str(data_shape))\n\u001b[0m\u001b[0;32m    574\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mcheck_batch_axis\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    575\u001b[0m           \u001b[0mdata_shape\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdata_shape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: Error when checking input: expected lstm_input to have 3 dimensions, but got array with shape (108, 13)"
     ]
    }
   ],
   "source": [
    "import json\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "import tensorflow.keras as keras\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "DATA_PATH = \"sick_sound.json\"\n",
    "\n",
    "\n",
    "def load_data(data_path):\n",
    "    \"\"\"Loads training dataset from json file.\n",
    "\n",
    "        :param data_path (str): Path to json file containing data\n",
    "        :return X (ndarray): Inputs\n",
    "        :return y (ndarray): Targets\n",
    "    \"\"\"\n",
    "\n",
    "    with open(data_path, \"r\") as fp:\n",
    "        data = json.load(fp)\n",
    "\n",
    "    X = np.array(data[\"mfcc\"])\n",
    "    y = np.array(data[\"labels\"])\n",
    "    return X, y\n",
    "\n",
    "\n",
    "def plot_history(history):\n",
    "    \"\"\"Plots accuracy/loss for training/validation set as a function of the epochs\n",
    "\n",
    "        :param history: Training history of model\n",
    "        :return:\n",
    "    \"\"\"\n",
    "\n",
    "    fig, axs = plt.subplots(2)\n",
    "\n",
    "    # create accuracy sublpot\n",
    "    axs[0].plot(history.history[\"accuracy\"], label=\"train accuracy\")\n",
    "    axs[0].plot(history.history[\"val_accuracy\"], label=\"test accuracy\")\n",
    "    axs[0].set_ylabel(\"Accuracy\")\n",
    "    axs[0].legend(loc=\"lower right\")\n",
    "    axs[0].set_title(\"Accuracy eval\")\n",
    "\n",
    "    # create error sublpot\n",
    "    axs[1].plot(history.history[\"loss\"], label=\"train error\")\n",
    "    axs[1].plot(history.history[\"val_loss\"], label=\"test error\")\n",
    "    axs[1].set_ylabel(\"Error\")\n",
    "    axs[1].set_xlabel(\"Epoch\")\n",
    "    axs[1].legend(loc=\"upper right\")\n",
    "    axs[1].set_title(\"Error eval\")\n",
    "\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "def prepare_datasets(test_size, validation_size):\n",
    "    \"\"\"Loads data and splits it into train, validation and test sets.\n",
    "\n",
    "    :param test_size (float): Value in [0, 1] indicating percentage of data set to allocate to test split\n",
    "    :param validation_size (float): Value in [0, 1] indicating percentage of train set to allocate to validation split\n",
    "\n",
    "    :return X_train (ndarray): Input training set\n",
    "    :return X_validation (ndarray): Input validation set\n",
    "    :return X_test (ndarray): Input test set\n",
    "    :return y_train (ndarray): Target training set\n",
    "    :return y_validation (ndarray): Target validation set\n",
    "    :return y_test (ndarray): Target test set\n",
    "    \"\"\"\n",
    "\n",
    "    # load data\n",
    "    X, y = load_data(DATA_PATH)\n",
    "\n",
    "    # create train, validation and test split\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size)\n",
    "    X_train, X_validation, y_train, y_validation = train_test_split(X_train, y_train, test_size=validation_size)\n",
    "\n",
    "    # FOr rnn we do not require extra dimension. All u required is just 3 dimensions i.e. [total samples, mfcc vector, mfcc_coefficient] \n",
    "\n",
    "    return X_train, X_validation, X_test, y_train, y_validation, y_test\n",
    "\n",
    "\n",
    "def build_model(input_shape):\n",
    "    \"\"\"Generate RNN model\n",
    "\n",
    "    :param input_shape (tuple): Shape of input set\n",
    "    :return model: RNN model\n",
    "    \"\"\"\n",
    "\n",
    "    # build network topology\n",
    "    model = keras.Sequential()\n",
    "\n",
    "    model.add(keras.layers.LSTM(100, input_shape = input_shape, return_sequences = True))\n",
    "    model.add(keras.layers.LSTM(100, return_sequences = True))\n",
    "    model.add(keras.layers.LSTM(100, return_sequences = True))\n",
    "    model.add(keras.layers.LSTM(100))\n",
    "\n",
    "\n",
    "    # output layer\n",
    "    model.add(keras.layers.Dense(112, activation='relu'))\n",
    "    model.add(keras.layers.Dropout(0.3))\n",
    "    model.add(keras.layers.Dense(2, activation='softmax'))\n",
    "\n",
    "\n",
    "    return model\n",
    "\n",
    "\n",
    "def predict(model, X, y):\n",
    "    \"\"\"Predict a single sample using the trained model\n",
    "\n",
    "    :param model: Trained classifier\n",
    "    :param X: Input data\n",
    "    :param y (int): Target\n",
    "    \"\"\"\n",
    "\n",
    "    # add a dimension to input data for sample - model.predict() expects a 4d array in this case\n",
    "    # X = X[np.newaxis, ...] # array shape (1, 130, 13, 1)\n",
    "    X = X[np.newaxis, ...] # array shape (130, 13, 1)\n",
    "\n",
    "\n",
    "    # perform prediction\n",
    "    prediction = model.predict(X)\n",
    "\n",
    "    # get index with max value\n",
    "    predicted_index = np.argmax(prediction, axis=1)\n",
    "\n",
    "    print(\"Target: {}, Predicted label: {}\".format(y, predicted_index))\n",
    "\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "\n",
    "    # get train, validation, test splits\n",
    "    X_train, X_validation, X_test, y_train, y_validation, y_test = prepare_datasets(0.25, 0.2)\n",
    "\n",
    "    # create network\n",
    "    input_shape = (X_train.shape[1], X_train.shape[2])\n",
    "    # Rnn takes input of 2 dims i.e. [130, 13] == [mfcc's vector, no of coefficients]\n",
    "    model = build_model(input_shape)\n",
    "\n",
    "    # compile model\n",
    "    optimizer = keras.optimizers.Adam(learning_rate=0.0001)\n",
    "    model.compile(optimizer=optimizer,\n",
    "                  loss='sparse_categorical_crossentropy',\n",
    "                  metrics=['accuracy'])\n",
    "    \n",
    "\n",
    "    model.summary()\n",
    "\n",
    "    # train model\n",
    "    history = model.fit(X_train, y_train, validation_data=(X_validation, y_validation), batch_size=32, epochs=30)\n",
    "\n",
    "    # plot accuracy/error for training and validation\n",
    "    plot_history(history)\n",
    "\n",
    "    # evaluate model on test set\n",
    "    test_loss, test_acc = model.evaluate(X_test, y_test, verbose=2)\n",
    "    print('\\nTest accuracy:', test_acc)\n",
    "\n",
    "    # pick a sample to predict from the test set\n",
    "    X_to_predict = X_test[100]\n",
    "    y_to_predict = y_test[100]\n",
    "\n",
    "    # predict sample\n",
    "    predict(model, X_to_predict, y_to_predict)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DOING PREDICTIONS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Target: 0, Predicted label: [0]\n"
     ]
    }
   ],
   "source": [
    "# create network\n",
    "input_shape = (X_train.shape[1], X_train.shape[2])\n",
    "model = build_model(input_shape)\n",
    "\n",
    "def predict(model, X, y):\n",
    "    \"\"\"Predict a single sample using the trained model\n",
    "\n",
    "    :param model: Trained classifier\n",
    "    :param X: Input data\n",
    "    :param y (int): Target\n",
    "    \"\"\"\n",
    "\n",
    "    # add a dimension to input data for sample - model.predict() expects a 4d array in this case\n",
    "    # X = X[np.newaxis, ...] # array shape (1, 130, 13, 1)\n",
    "    X = X[np.newaxis, ...] # array shape (130, 13, 1)\n",
    "\n",
    "\n",
    "    # perform prediction\n",
    "    prediction = model.predict(X)\n",
    "\n",
    "    # get index with max value\n",
    "    predicted_index = np.argmax(prediction, axis=1)\n",
    "\n",
    "    print(\"Target: {}, Predicted label: {}\".format(y, predicted_index))\n",
    "\n",
    "\n",
    "X_to_predict = X_test[12]\n",
    "y_to_predict = y_test[12]\n",
    "\n",
    "predict(model, X_to_predict, y_to_predict)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "outputs": [],
   "source": []
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   "cell_type": "code",
   "execution_count": null,
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   "source": []
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   "cell_type": "code",
   "execution_count": null,
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   "source": []
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   "cell_type": "code",
   "execution_count": null,
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   "source": []
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   "cell_type": "code",
   "execution_count": null,
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   "source": []
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "outputs": [],
   "source": []
  }
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