Training.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "## Import packages"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "import numpy as np\n",
    "import random\n",
    "import librosa.display\n",
    "import tensorflow_io as tfio\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.utils import to_categorical\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "from tensorflow.keras.layers import Conv2D, Flatten, Dense, Dropout, MaxPool2D\n",
    "from tensorflow.keras import Sequential\n",
    "from tensorflow.keras.callbacks import ModelCheckpoint\n",
    "from helperFunctions import create_dataset_df\n",
    "import matplotlib.pyplot as plt"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Import dataset metadata"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [
    {
     "data": {
      "text/plain": "      slice_file_name    fsID  start        end  salience  fold  classID  \\\n0    100032-3-0-0.wav  100032    0.0   0.317551         1     5        3   \n1  100263-2-0-117.wav  100263   58.5  62.500000         1     5        2   \n2  100263-2-0-121.wav  100263   60.5  64.500000         1     5        2   \n3  100263-2-0-126.wav  100263   63.0  67.000000         1     5        2   \n4  100263-2-0-137.wav  100263   68.5  72.500000         1     5        2   \n\n              class                                     filepath  \n0          dog_bark    UrbanSound8K/audio\\fold5\\100032-3-0-0.wav  \n1  children_playing  UrbanSound8K/audio\\fold5\\100263-2-0-117.wav  \n2  children_playing  UrbanSound8K/audio\\fold5\\100263-2-0-121.wav  \n3  children_playing  UrbanSound8K/audio\\fold5\\100263-2-0-126.wav  \n4  children_playing  UrbanSound8K/audio\\fold5\\100263-2-0-137.wav  ",
      "text/html": "<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>slice_file_name</th>\n      <th>fsID</th>\n      <th>start</th>\n      <th>end</th>\n      <th>salience</th>\n      <th>fold</th>\n      <th>classID</th>\n      <th>class</th>\n      <th>filepath</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>100032-3-0-0.wav</td>\n      <td>100032</td>\n      <td>0.0</td>\n      <td>0.317551</td>\n      <td>1</td>\n      <td>5</td>\n      <td>3</td>\n      <td>dog_bark</td>\n      <td>UrbanSound8K/audio\\fold5\\100032-3-0-0.wav</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>100263-2-0-117.wav</td>\n      <td>100263</td>\n      <td>58.5</td>\n      <td>62.500000</td>\n      <td>1</td>\n      <td>5</td>\n      <td>2</td>\n      <td>children_playing</td>\n      <td>UrbanSound8K/audio\\fold5\\100263-2-0-117.wav</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>100263-2-0-121.wav</td>\n      <td>100263</td>\n      <td>60.5</td>\n      <td>64.500000</td>\n      <td>1</td>\n      <td>5</td>\n      <td>2</td>\n      <td>children_playing</td>\n      <td>UrbanSound8K/audio\\fold5\\100263-2-0-121.wav</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>100263-2-0-126.wav</td>\n      <td>100263</td>\n      <td>63.0</td>\n      <td>67.000000</td>\n      <td>1</td>\n      <td>5</td>\n      <td>2</td>\n      <td>children_playing</td>\n      <td>UrbanSound8K/audio\\fold5\\100263-2-0-126.wav</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>100263-2-0-137.wav</td>\n      <td>100263</td>\n      <td>68.5</td>\n      <td>72.500000</td>\n      <td>1</td>\n      <td>5</td>\n      <td>2</td>\n      <td>children_playing</td>\n      <td>UrbanSound8K/audio\\fold5\\100263-2-0-137.wav</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_df = create_dataset_df('UrbanSound8K/metadata/UrbanSound8K.csv')\n",
    "dataset_df.head()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Feature Extraction and Data Augmentation functions\n",
    "When analyzing the data, we found that our dataset contains different sampling frequencies, length, mono / stereo, etc.\n",
    "In order to classify the data using their spectrogram, we must unify them so that they have the same sampling frequency, length and number of channels.<br>\n",
    "\n",
    "We will be using Mel-spectrogram for audio classification. If we were classifying speech and not general sounds, we would probably use MFCCs, which generally perform better in these kinds of tasks.\n",
    "Created spectrum is already one-sided, so we don´t need to cut of second half because of duplicate information.\n",
    "By default used window is *Hann Window*, and we will keep it this way.<br>\n",
    "\n",
    "Regarding augmentation, we will apply time shift, time masking and freq masking, which should help with generalization of the model"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [],
   "source": [
    "CLASSES = 10\n",
    "SAMPLE_RATE = 22050 #Hz\n",
    "DURATION = 4 #s\n",
    "N_FFT = 1024\n",
    "HOP_LENGTH = 256\n",
    "N_MELS = 64\n",
    "TIME_BINS =  np.ceil(DURATION*SAMPLE_RATE/N_FFT*(N_FFT/HOP_LENGTH)).astype('int')\n",
    "TIME_SHIFT_LIMIT = 0.5\n",
    "TIME_MASKING_PARAM = np.round(TIME_BINS*0.1) # Up to 10% of time can be masked out\n",
    "FREQ_MASKING_PARAM = np.round(N_MELS*0.1) # Up to 10% of Freqs can be masked out\n",
    "\n",
    "def processing(filepath, class_id, augment=False):\n",
    "    # Load audio as mono and resampled\n",
    "    audio, sample_rate = librosa.load(filepath.numpy(),\n",
    "                                      sr=SAMPLE_RATE,\n",
    "                                      mono=True)\n",
    "\n",
    "    # Make sure every signal has desired length\n",
    "    audio = librosa.util.fix_length(audio,\n",
    "                                    size= DURATION * SAMPLE_RATE)\n",
    "\n",
    "    # Shift data by random amount to the right/left\n",
    "    if augment:\n",
    "        audio = time_shift(audio,\n",
    "                            shift_limit=TIME_SHIFT_LIMIT)\n",
    "\n",
    "    # Convert to freq domain. -> Mel Spectrum\n",
    "    spec = mel_dB_spectrum(audio,\n",
    "                                sr=SAMPLE_RATE,\n",
    "                                n_fft=N_FFT,\n",
    "                                hop_length=HOP_LENGTH,\n",
    "                                n_mels=N_MELS)\n",
    "    # Add Time and Freq masking\n",
    "    if augment:\n",
    "        spec = tfio.audio.freq_mask(np.transpose(spec), param=FREQ_MASKING_PARAM)\n",
    "        spec = np.transpose(tfio.audio.time_mask(spec, param=TIME_MASKING_PARAM))\n",
    "\n",
    "    x = np.reshape(spec, (N_MELS, TIME_BINS, 1))\n",
    "    y = to_categorical(class_id, CLASSES)\n",
    "    return x, y\n",
    "\n",
    "def time_shift(audio, shift_limit):\n",
    "    audio_len = audio.shape[0]\n",
    "    shift_percentage = random.uniform(-1, 1) * shift_limit\n",
    "    shift_amt = int(shift_percentage * audio_len)\n",
    "    return np.roll(audio, shift_amt)\n",
    "\n",
    "def mel_dB_spectrum(audio, sr, n_fft, hop_length, n_mels):\n",
    "    mel = librosa.feature.melspectrogram(y=audio,\n",
    "                                          sr=sr,\n",
    "                                          n_fft=n_fft,\n",
    "                                          hop_length=hop_length,\n",
    "                                          n_mels=n_mels)\n",
    "    return librosa.power_to_db(np.abs(mel))\n",
    "\n",
    "def fixup_shape(data, labels):\n",
    "    data.set_shape([N_MELS, TIME_BINS, 1])\n",
    "    labels.set_shape([CLASSES])\n",
    "    return data, labels"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Model Architecture\n",
    "Regarding model architecture I wanted to try design it myself in order get practical experience with model structures and effect of its parameters."
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [],
   "source": [
    "def create_model(features_size):\n",
    "    model = Sequential()\n",
    "\n",
    "    model.add(Conv2D(filters=32,\n",
    "                    kernel_size=(3,3),\n",
    "                    padding='valid',\n",
    "                    input_shape=features_size,\n",
    "                    activation='relu'))\n",
    "    model.add(MaxPool2D(pool_size=(2,2)))\n",
    "\n",
    "    model.add(Conv2D(filters=32,\n",
    "                  kernel_size=(3,3),\n",
    "                  padding='valid',\n",
    "                  activation='relu'))\n",
    "    model.add(MaxPool2D(pool_size=(2,2)))\n",
    "    model.add(Dropout(0.2))\n",
    "\n",
    "    model.add(Conv2D(filters=64,\n",
    "                  kernel_size=(3,3),\n",
    "                  padding='valid',\n",
    "                  activation='relu'))\n",
    "    model.add(MaxPool2D(pool_size=(2,2)))\n",
    "\n",
    "    model.add(Conv2D(filters=64,\n",
    "                  kernel_size=(3,3),\n",
    "                  padding='same',\n",
    "                  activation='relu'))\n",
    "    model.add(MaxPool2D(pool_size=(2,2)))\n",
    "    model.add(Dropout(0.2))\n",
    "\n",
    "    model.add(Flatten())\n",
    "    model.add(Dense(256, activation='relu'))\n",
    "    model.add(Dropout(0.5))\n",
    "    model.add(Dense(128, activation='relu'))\n",
    "    model.add(Dense(10, activation='softmax'))\n",
    "\n",
    "    optimizer = Adam(learning_rate= 0.001)\n",
    "\n",
    "    model.compile(optimizer, loss='categorical_crossentropy', metrics=[\"accuracy\"])\n",
    "    return model"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d (Conv2D)             (None, 62, 343, 32)       320       \n",
      "                                                                 \n",
      " max_pooling2d (MaxPooling2D  (None, 31, 171, 32)      0         \n",
      " )                                                               \n",
      "                                                                 \n",
      " conv2d_1 (Conv2D)           (None, 29, 169, 32)       9248      \n",
      "                                                                 \n",
      " max_pooling2d_1 (MaxPooling  (None, 14, 84, 32)       0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " dropout (Dropout)           (None, 14, 84, 32)        0         \n",
      "                                                                 \n",
      " conv2d_2 (Conv2D)           (None, 12, 82, 64)        18496     \n",
      "                                                                 \n",
      " max_pooling2d_2 (MaxPooling  (None, 6, 41, 64)        0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " conv2d_3 (Conv2D)           (None, 6, 41, 64)         36928     \n",
      "                                                                 \n",
      " max_pooling2d_3 (MaxPooling  (None, 3, 20, 64)        0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " dropout_1 (Dropout)         (None, 3, 20, 64)         0         \n",
      "                                                                 \n",
      " flatten (Flatten)           (None, 3840)              0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 256)               983296    \n",
      "                                                                 \n",
      " dropout_2 (Dropout)         (None, 256)               0         \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 128)               32896     \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 10)                1290      \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 1,082,474\n",
      "Trainable params: 1,082,474\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "model = create_model((N_MELS, TIME_BINS, 1))\n",
    "print(model.summary())"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Dataset split\n",
    "\n",
    "We should use Kfold cross validation, but as part of speeding up and demonstrating the principle, we will do it as follows:\n",
    "\n",
    "- For training we will use folds 1-9\n",
    "- For validating we will use fold 10\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [],
   "source": [
    "BATCH_SIZE = 32"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Training Dataset"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [],
   "source": [
    "def create_training_dataset(dataframe):\n",
    "    return  tf.data.Dataset.from_tensor_slices((dataframe[\"filepath\"], dataframe[\"classID\"]))\\\n",
    "                    .map(lambda filepath, class_id: tf.py_function(processing,\n",
    "                                                  inp=[filepath, class_id, True],\n",
    "                                                  Tout=[tf.float32, tf.int32]),\n",
    "                          num_parallel_calls=tf.data.AUTOTUNE)\\\n",
    "                    .map(fixup_shape,\n",
    "                          num_parallel_calls=tf.data.AUTOTUNE)\\\n",
    "                    .batch(BATCH_SIZE)\\\n",
    "                    .prefetch(tf.data.AUTOTUNE)\\\n",
    "                    .shuffle(len(dataframe)//BATCH_SIZE)\\\n",
    "                    .repeat()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [],
   "source": [
    "train_df = dataset_df.loc[dataset_df[\"fold\"] < 10]\n",
    "\n",
    "train_dataset = create_training_dataset(train_df)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Augmented data example"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       slice_file_name    fsID  start  end  salience  fold  classID     class  \\\n",
      "2310  155314-3-0-5.wav  155314    2.5  6.5         1     7        3  dog_bark   \n",
      "\n",
      "                                       filepath  \n",
      "2310  UrbanSound8K/audio\\fold7\\155314-3-0-5.wav  \n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 1080x1080 with 3 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = train_df.sample(1)\n",
    "print(df)\n",
    "#Create dataset, this will return object of TensorSliceDataset\n",
    "dataset = tf.data.Dataset.from_tensor_slices((df[\"filepath\"], df[\"classID\"]))\\\n",
    "                    .map(lambda filepath, class_id: tf.py_function(processing,\n",
    "                                                  inp=[filepath, class_id, True],\n",
    "                                                  Tout=[tf.float32, tf.int32]),\n",
    "                          num_parallel_calls=tf.data.AUTOTUNE)\\\n",
    "                    .map(fixup_shape,\n",
    "                          num_parallel_calls=tf.data.AUTOTUNE)\\\n",
    "                    .repeat()\\\n",
    "                    .cache()\\\n",
    "                    .prefetch(tf.data.AUTOTUNE)\\\n",
    "\n",
    "\n",
    "# Visualize the Mel-Spectograms\n",
    "fig, axs = plt.subplots(3, figsize=(15,15))\n",
    "row = 0\n",
    "for data, label in dataset:\n",
    "        data = np.reshape(data.numpy(),(N_MELS, TIME_BINS))\n",
    "        spec = librosa.display.specshow(data,\n",
    "                                        sr=SAMPLE_RATE,\n",
    "                                        hop_length=HOP_LENGTH,\n",
    "                                        x_axis=\"time\",\n",
    "                                        y_axis='mel',\n",
    "                                        ax=axs[row])\n",
    "        row += 1\n",
    "        if row == 3:\n",
    "            break\n",
    "\n",
    "fig.tight_layout()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Validation Dataset"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [],
   "source": [
    "def create_validation_dataset(dataframe):\n",
    "    return tf.data.Dataset.from_tensor_slices((dataframe[\"filepath\"], dataframe[\"classID\"]))\\\n",
    "                    .map(lambda filepath, class_id: tf.py_function(processing,\n",
    "                                                  inp=[filepath, class_id],\n",
    "                                                  Tout=[tf.float32, tf.int32]),\n",
    "                          num_parallel_calls=tf.data.AUTOTUNE)\\\n",
    "                    .map(fixup_shape,\n",
    "                          num_parallel_calls=tf.data.AUTOTUNE)\\\n",
    "                    .cache()\\\n",
    "                    .batch(BATCH_SIZE)\\\n",
    "                    .prefetch(tf.data.AUTOTUNE)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [],
   "source": [
    "valid_df = dataset_df.loc[dataset_df[\"fold\"] == 10]\n",
    "\n",
    "valid_dataset = create_validation_dataset(valid_df)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Training"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Model saving"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "outputs": [],
   "source": [
    "checkpoint_path = \"models/acusticEvents-{epoch:04d}.ckpt\"\n",
    "checkpoint_dir = os.path.dirname(checkpoint_path)\n",
    "\n",
    "# Create a callback that saves the model's weights every epoch\n",
    "model_save_callback = ModelCheckpoint(\n",
    "    filepath=checkpoint_path,\n",
    "    verbose=1,\n",
    "    save_weights_only=True,\n",
    "    save_freq=len(train_df)//BATCH_SIZE)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Clean training"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/40\n",
      "245/246 [============================>.] - ETA: 1s - loss: 2.5151 - accuracy: 0.1167\n",
      "Epoch 1: saving model to models\\acusticEvents-0001.ckpt\n",
      "246/246 [==============================] - 797s 2s/step - loss: 2.5139 - accuracy: 0.1180 - val_loss: 2.2776 - val_accuracy: 0.1142\n",
      "Epoch 2/40\n",
      "245/246 [============================>.] - ETA: 1s - loss: 2.2728 - accuracy: 0.1265\n",
      "Epoch 2: saving model to models\\acusticEvents-0002.ckpt\n",
      "246/246 [==============================] - 416s 2s/step - loss: 2.2726 - accuracy: 0.1262 - val_loss: 2.2298 - val_accuracy: 0.1406\n",
      "Epoch 3/40\n",
      "245/246 [============================>.] - ETA: 1s - loss: 2.1672 - accuracy: 0.1923\n",
      "Epoch 3: saving model to models\\acusticEvents-0003.ckpt\n",
      "246/246 [==============================] - 415s 2s/step - loss: 2.1666 - accuracy: 0.1925 - val_loss: 2.0741 - val_accuracy: 0.2981\n",
      "Epoch 4/40\n",
      "245/246 [============================>.] - ETA: 1s - loss: 2.0303 - accuracy: 0.2536\n",
      "Epoch 4: saving model to models\\acusticEvents-0004.ckpt\n",
      "246/246 [==============================] - 416s 2s/step - loss: 2.0254 - accuracy: 0.2560 - val_loss: 1.9480 - val_accuracy: 0.2500\n",
      "Epoch 5/40\n",
      "245/246 [============================>.] - ETA: 1s - loss: 1.9493 - accuracy: 0.2760\n",
      "Epoch 5: saving model to models\\acusticEvents-0005.ckpt\n",
      "246/246 [==============================] - 418s 2s/step - loss: 1.9478 - accuracy: 0.2760 - val_loss: 1.8574 - val_accuracy: 0.2885\n",
      "Epoch 6/40\n",
      "245/246 [============================>.] - ETA: 1s - loss: 1.8232 - accuracy: 0.3278\n",
      "Epoch 6: saving model to models\\acusticEvents-0006.ckpt\n",
      "246/246 [==============================] - 418s 2s/step - loss: 1.8238 - accuracy: 0.3274 - val_loss: 1.7895 - val_accuracy: 0.3438\n",
      "Epoch 7/40\n",
      "245/246 [============================>.] - ETA: 1s - loss: 1.6887 - accuracy: 0.3862\n",
      "Epoch 7: saving model to models\\acusticEvents-0007.ckpt\n",
      "246/246 [==============================] - 417s 2s/step - loss: 1.6890 - accuracy: 0.3856 - val_loss: 1.8108 - val_accuracy: 0.3702\n",
      "Epoch 8/40\n",
      "245/246 [============================>.] - ETA: 1s - loss: 1.5764 - accuracy: 0.4266\n",
      "Epoch 8: saving model to models\\acusticEvents-0008.ckpt\n",
      "246/246 [==============================] - 415s 2s/step - loss: 1.5781 - accuracy: 0.4258 - val_loss: 1.6375 - val_accuracy: 0.4291\n",
      "Epoch 9/40\n",
      "245/246 [============================>.] - ETA: 1s - loss: 1.5186 - accuracy: 0.4533\n",
      "Epoch 9: saving model to models\\acusticEvents-0009.ckpt\n",
      "246/246 [==============================] - 417s 2s/step - loss: 1.5154 - accuracy: 0.4550 - val_loss: 1.6180 - val_accuracy: 0.3858\n",
      "Epoch 10/40\n",
      "245/246 [============================>.] - ETA: 1s - loss: 1.4256 - accuracy: 0.4773\n",
      "Epoch 10: saving model to models\\acusticEvents-0010.ckpt\n",
      "246/246 [==============================] - 417s 2s/step - loss: 1.4250 - accuracy: 0.4781 - val_loss: 1.6072 - val_accuracy: 0.4327\n",
      "Epoch 11/40\n",
      "245/246 [============================>.] - ETA: 1s - loss: 1.4155 - accuracy: 0.4778\n",
      "Epoch 11: saving model to models\\acusticEvents-0011.ckpt\n",
      "246/246 [==============================] - 412s 2s/step - loss: 1.4153 - accuracy: 0.4782 - val_loss: 1.4609 - val_accuracy: 0.4892\n",
      "Epoch 12/40\n",
      "245/246 [============================>.] - ETA: 1s - loss: 1.3291 - accuracy: 0.5182\n",
      "Epoch 12: saving model to models\\acusticEvents-0012.ckpt\n",
      "246/246 [==============================] - 415s 2s/step - loss: 1.3267 - accuracy: 0.5193 - val_loss: 1.4839 - val_accuracy: 0.4964\n",
      "Epoch 13/40\n",
      "245/246 [============================>.] - ETA: 1s - loss: 1.2597 - accuracy: 0.5481\n",
      "Epoch 13: saving model to models\\acusticEvents-0013.ckpt\n",
      "246/246 [==============================] - 415s 2s/step - loss: 1.2574 - accuracy: 0.5494 - val_loss: 1.5495 - val_accuracy: 0.4651\n",
      "Epoch 14/40\n",
      "245/246 [============================>.] - ETA: 1s - loss: 1.2104 - accuracy: 0.5624\n",
      "Epoch 14: saving model to models\\acusticEvents-0014.ckpt\n",
      "246/246 [==============================] - 419s 2s/step - loss: 1.2100 - accuracy: 0.5625 - val_loss: 1.4044 - val_accuracy: 0.5661\n",
      "Epoch 15/40\n",
      "245/246 [============================>.] - ETA: 1s - loss: 1.1959 - accuracy: 0.5740\n",
      "Epoch 15: saving model to models\\acusticEvents-0015.ckpt\n",
      "246/246 [==============================] - 414s 2s/step - loss: 1.1977 - accuracy: 0.5731 - val_loss: 1.2894 - val_accuracy: 0.5877\n",
      "Epoch 16/40\n",
      "245/246 [============================>.] - ETA: 1s - loss: 1.1272 - accuracy: 0.5908\n",
      "Epoch 16: saving model to models\\acusticEvents-0016.ckpt\n",
      "246/246 [==============================] - 412s 2s/step - loss: 1.1265 - accuracy: 0.5918 - val_loss: 1.3003 - val_accuracy: 0.5661\n",
      "Epoch 17/40\n",
      "245/246 [============================>.] - ETA: 1s - loss: 1.0852 - accuracy: 0.6068\n",
      "Epoch 17: saving model to models\\acusticEvents-0017.ckpt\n",
      "246/246 [==============================] - 414s 2s/step - loss: 1.0885 - accuracy: 0.6059 - val_loss: 1.2169 - val_accuracy: 0.6082\n",
      "Epoch 18/40\n",
      "245/246 [============================>.] - ETA: 1s - loss: 1.0840 - accuracy: 0.6161\n",
      "Epoch 18: saving model to models\\acusticEvents-0018.ckpt\n",
      "246/246 [==============================] - 411s 2s/step - loss: 1.0852 - accuracy: 0.6150 - val_loss: 1.3789 - val_accuracy: 0.5072\n",
      "Epoch 19/40\n",
      "245/246 [============================>.] - ETA: 1s - loss: 1.0699 - accuracy: 0.6113\n",
      "Epoch 19: saving model to models\\acusticEvents-0019.ckpt\n",
      "246/246 [==============================] - 414s 2s/step - loss: 1.0700 - accuracy: 0.6113 - val_loss: 1.2565 - val_accuracy: 0.6010\n",
      "Epoch 20/40\n",
      "245/246 [============================>.] - ETA: 1s - loss: 0.9790 - accuracy: 0.6506\n",
      "Epoch 20: saving model to models\\acusticEvents-0020.ckpt\n",
      "246/246 [==============================] - 410s 2s/step - loss: 0.9821 - accuracy: 0.6490 - val_loss: 1.1561 - val_accuracy: 0.5938\n",
      "Epoch 21/40\n",
      "245/246 [============================>.] - ETA: 1s - loss: 0.9715 - accuracy: 0.6562\n",
      "Epoch 21: saving model to models\\acusticEvents-0021.ckpt\n",
      "246/246 [==============================] - 411s 2s/step - loss: 0.9693 - accuracy: 0.6574 - val_loss: 1.3119 - val_accuracy: 0.5709\n",
      "Epoch 22/40\n",
      "245/246 [============================>.] - ETA: 1s - loss: 0.9454 - accuracy: 0.6700\n",
      "Epoch 22: saving model to models\\acusticEvents-0022.ckpt\n",
      "246/246 [==============================] - 412s 2s/step - loss: 0.9448 - accuracy: 0.6701 - val_loss: 1.0053 - val_accuracy: 0.6683\n",
      "Epoch 23/40\n",
      "245/246 [============================>.] - ETA: 1s - loss: 0.9320 - accuracy: 0.6688\n",
      "Epoch 23: saving model to models\\acusticEvents-0023.ckpt\n",
      "246/246 [==============================] - 413s 2s/step - loss: 0.9304 - accuracy: 0.6693 - val_loss: 1.1193 - val_accuracy: 0.6238\n",
      "Epoch 24/40\n",
      "245/246 [============================>.] - ETA: 1s - loss: 0.8433 - accuracy: 0.7049\n",
      "Epoch 24: saving model to models\\acusticEvents-0024.ckpt\n",
      "246/246 [==============================] - 416s 2s/step - loss: 0.8434 - accuracy: 0.7046 - val_loss: 1.3303 - val_accuracy: 0.6118\n",
      "Epoch 25/40\n",
      "245/246 [============================>.] - ETA: 1s - loss: 0.8845 - accuracy: 0.6906\n",
      "Epoch 25: saving model to models\\acusticEvents-0025.ckpt\n",
      "246/246 [==============================] - 414s 2s/step - loss: 0.8859 - accuracy: 0.6898 - val_loss: 1.1583 - val_accuracy: 0.6526\n",
      "Epoch 26/40\n",
      "245/246 [============================>.] - ETA: 1s - loss: 0.8711 - accuracy: 0.6981\n",
      "Epoch 26: saving model to models\\acusticEvents-0026.ckpt\n",
      "246/246 [==============================] - 413s 2s/step - loss: 0.8676 - accuracy: 0.6994 - val_loss: 1.2428 - val_accuracy: 0.6226\n",
      "Epoch 27/40\n",
      "245/246 [============================>.] - ETA: 1s - loss: 0.8110 - accuracy: 0.7132\n",
      "Epoch 27: saving model to models\\acusticEvents-0027.ckpt\n",
      "246/246 [==============================] - 417s 2s/step - loss: 0.8126 - accuracy: 0.7122 - val_loss: 1.1538 - val_accuracy: 0.6382\n",
      "Epoch 28/40\n",
      "245/246 [============================>.] - ETA: 1s - loss: 0.7960 - accuracy: 0.7203\n",
      "Epoch 28: saving model to models\\acusticEvents-0028.ckpt\n",
      "246/246 [==============================] - 413s 2s/step - loss: 0.7953 - accuracy: 0.7207 - val_loss: 1.2198 - val_accuracy: 0.6202\n",
      "Epoch 29/40\n",
      "245/246 [============================>.] - ETA: 1s - loss: 0.7824 - accuracy: 0.7322\n",
      "Epoch 29: saving model to models\\acusticEvents-0029.ckpt\n",
      "246/246 [==============================] - 412s 2s/step - loss: 0.7835 - accuracy: 0.7311 - val_loss: 1.0795 - val_accuracy: 0.6406\n",
      "Epoch 30/40\n",
      "245/246 [============================>.] - ETA: 1s - loss: 0.7943 - accuracy: 0.7182\n",
      "Epoch 30: saving model to models\\acusticEvents-0030.ckpt\n",
      "246/246 [==============================] - 415s 2s/step - loss: 0.7911 - accuracy: 0.7194 - val_loss: 1.2861 - val_accuracy: 0.5938\n",
      "Epoch 31/40\n",
      "245/246 [============================>.] - ETA: 1s - loss: 0.7255 - accuracy: 0.7460\n",
      "Epoch 31: saving model to models\\acusticEvents-0031.ckpt\n",
      "246/246 [==============================] - 410s 2s/step - loss: 0.7242 - accuracy: 0.7464 - val_loss: 1.2801 - val_accuracy: 0.6298\n",
      "Epoch 32/40\n",
      "245/246 [============================>.] - ETA: 1s - loss: 0.7859 - accuracy: 0.7244\n",
      "Epoch 32: saving model to models\\acusticEvents-0032.ckpt\n",
      "246/246 [==============================] - 416s 2s/step - loss: 0.7855 - accuracy: 0.7244 - val_loss: 1.0495 - val_accuracy: 0.6755\n",
      "Epoch 33/40\n",
      "245/246 [============================>.] - ETA: 1s - loss: 0.7119 - accuracy: 0.7472\n",
      "Epoch 33: saving model to models\\acusticEvents-0033.ckpt\n",
      "246/246 [==============================] - 415s 2s/step - loss: 0.7156 - accuracy: 0.7463 - val_loss: 1.3337 - val_accuracy: 0.6274\n",
      "Epoch 34/40\n",
      "245/246 [============================>.] - ETA: 1s - loss: 0.7170 - accuracy: 0.7525\n",
      "Epoch 34: saving model to models\\acusticEvents-0034.ckpt\n",
      "246/246 [==============================] - 409s 2s/step - loss: 0.7156 - accuracy: 0.7530 - val_loss: 1.0895 - val_accuracy: 0.6575\n",
      "Epoch 35/40\n",
      "245/246 [============================>.] - ETA: 1s - loss: 0.7068 - accuracy: 0.7557\n",
      "Epoch 35: saving model to models\\acusticEvents-0035.ckpt\n",
      "246/246 [==============================] - 409s 2s/step - loss: 0.7071 - accuracy: 0.7554 - val_loss: 1.1198 - val_accuracy: 0.6911\n",
      "Epoch 36/40\n",
      "245/246 [============================>.] - ETA: 1s - loss: 0.6419 - accuracy: 0.7764\n",
      "Epoch 36: saving model to models\\acusticEvents-0036.ckpt\n",
      "246/246 [==============================] - 410s 2s/step - loss: 0.6453 - accuracy: 0.7748 - val_loss: 1.1858 - val_accuracy: 0.6502\n",
      "Epoch 37/40\n",
      "245/246 [============================>.] - ETA: 1s - loss: 0.6548 - accuracy: 0.7653\n",
      "Epoch 37: saving model to models\\acusticEvents-0037.ckpt\n",
      "246/246 [==============================] - 414s 2s/step - loss: 0.6559 - accuracy: 0.7648 - val_loss: 1.1123 - val_accuracy: 0.6875\n",
      "Epoch 38/40\n",
      "245/246 [============================>.] - ETA: 1s - loss: 0.6749 - accuracy: 0.7613\n",
      "Epoch 38: saving model to models\\acusticEvents-0038.ckpt\n",
      "246/246 [==============================] - 414s 2s/step - loss: 0.6748 - accuracy: 0.7614 - val_loss: 1.2277 - val_accuracy: 0.6346\n",
      "Epoch 39/40\n",
      "245/246 [============================>.] - ETA: 1s - loss: 0.6246 - accuracy: 0.7747\n",
      "Epoch 39: saving model to models\\acusticEvents-0039.ckpt\n",
      "246/246 [==============================] - 413s 2s/step - loss: 0.6272 - accuracy: 0.7735 - val_loss: 1.1566 - val_accuracy: 0.6262\n",
      "Epoch 40/40\n",
      "245/246 [============================>.] - ETA: 1s - loss: 0.6211 - accuracy: 0.7879\n",
      "Epoch 40: saving model to models\\acusticEvents-0040.ckpt\n",
      "246/246 [==============================] - 415s 2s/step - loss: 0.6229 - accuracy: 0.7866 - val_loss: 1.0774 - val_accuracy: 0.6827\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(train_dataset,\n",
    "              validation_data=valid_dataset,\n",
    "              validation_steps=len(valid_df)//BATCH_SIZE,\n",
    "              steps_per_epoch=len(train_df)//BATCH_SIZE,\n",
    "              epochs=40,\n",
    "              callbacks=[model_save_callback])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Training from checkpoint"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "245/246 [============================>.] - ETA: 1s - loss: 0.6108 - accuracy: 0.7820\n",
      "Epoch 1: saving model to models\\acusticEvents-0001.ckpt\n",
      "246/246 [==============================] - 757s 2s/step - loss: 0.6110 - accuracy: 0.7820 - val_loss: 1.0708 - val_accuracy: 0.6983\n",
      "Epoch 2/100\n",
      "245/246 [============================>.] - ETA: 1s - loss: 0.5709 - accuracy: 0.8036\n",
      "Epoch 2: saving model to models\\acusticEvents-0002.ckpt\n",
      "246/246 [==============================] - 400s 2s/step - loss: 0.5716 - accuracy: 0.8034 - val_loss: 1.0592 - val_accuracy: 0.6803\n",
      "Epoch 3/100\n",
      "245/246 [============================>.] - ETA: 1s - loss: 0.6143 - accuracy: 0.7953\n",
      "Epoch 3: saving model to models\\acusticEvents-0003.ckpt\n",
      "246/246 [==============================] - 406s 2s/step - loss: 0.6145 - accuracy: 0.7951 - val_loss: 1.1980 - val_accuracy: 0.6815\n",
      "Epoch 4/100\n",
      "245/246 [============================>.] - ETA: 1s - loss: 0.5514 - accuracy: 0.8104\n",
      "Epoch 4: saving model to models\\acusticEvents-0004.ckpt\n",
      "246/246 [==============================] - 408s 2s/step - loss: 0.5515 - accuracy: 0.8104 - val_loss: 1.1161 - val_accuracy: 0.7115\n",
      "Epoch 5/100\n",
      "245/246 [============================>.] - ETA: 1s - loss: 0.5847 - accuracy: 0.7990\n",
      "Epoch 5: saving model to models\\acusticEvents-0005.ckpt\n",
      "246/246 [==============================] - 405s 2s/step - loss: 0.5836 - accuracy: 0.7993 - val_loss: 1.2070 - val_accuracy: 0.6695\n",
      "Epoch 6/100\n",
      "245/246 [============================>.] - ETA: 1s - loss: 0.5599 - accuracy: 0.8056\n",
      "Epoch 6: saving model to models\\acusticEvents-0006.ckpt\n",
      "246/246 [==============================] - 407s 2s/step - loss: 0.5590 - accuracy: 0.8061 - val_loss: 1.0754 - val_accuracy: 0.6875\n",
      "Epoch 7/100\n",
      "245/246 [============================>.] - ETA: 1s - loss: 0.5453 - accuracy: 0.8104\n",
      "Epoch 7: saving model to models\\acusticEvents-0007.ckpt\n",
      "246/246 [==============================] - 408s 2s/step - loss: 0.5449 - accuracy: 0.8104 - val_loss: 1.0412 - val_accuracy: 0.6923\n",
      "Epoch 8/100\n",
      "245/246 [============================>.] - ETA: 1s - loss: 0.5485 - accuracy: 0.8102\n",
      "Epoch 8: saving model to models\\acusticEvents-0008.ckpt\n",
      "246/246 [==============================] - 407s 2s/step - loss: 0.5487 - accuracy: 0.8105 - val_loss: 1.1949 - val_accuracy: 0.6791\n",
      "Epoch 9/100\n",
      "245/246 [============================>.] - ETA: 1s - loss: 0.6063 - accuracy: 0.7885\n",
      "Epoch 9: saving model to models\\acusticEvents-0009.ckpt\n",
      "246/246 [==============================] - 406s 2s/step - loss: 0.6065 - accuracy: 0.7889 - val_loss: 1.2416 - val_accuracy: 0.6899\n",
      "Epoch 10/100\n",
      "245/246 [============================>.] - ETA: 1s - loss: 0.5330 - accuracy: 0.8122\n",
      "Epoch 10: saving model to models\\acusticEvents-0010.ckpt\n",
      "246/246 [==============================] - 404s 2s/step - loss: 0.5346 - accuracy: 0.8113 - val_loss: 1.2604 - val_accuracy: 0.7019\n",
      "Epoch 11/100\n",
      "245/246 [============================>.] - ETA: 1s - loss: 0.5244 - accuracy: 0.8185\n",
      "Epoch 11: saving model to models\\acusticEvents-0011.ckpt\n",
      "246/246 [==============================] - 409s 2s/step - loss: 0.5250 - accuracy: 0.8184 - val_loss: 1.2054 - val_accuracy: 0.6514\n",
      "Epoch 12/100\n",
      "245/246 [============================>.] - ETA: 1s - loss: 0.4988 - accuracy: 0.8266\n",
      "Epoch 12: saving model to models\\acusticEvents-0012.ckpt\n",
      "246/246 [==============================] - 412s 2s/step - loss: 0.4989 - accuracy: 0.8267 - val_loss: 1.0548 - val_accuracy: 0.7007\n",
      "Epoch 13/100\n",
      "245/246 [============================>.] - ETA: 1s - loss: 0.5125 - accuracy: 0.8202\n",
      "Epoch 13: saving model to models\\acusticEvents-0013.ckpt\n",
      "246/246 [==============================] - 408s 2s/step - loss: 0.5104 - accuracy: 0.8209 - val_loss: 1.1368 - val_accuracy: 0.7007\n",
      "Epoch 14/100\n",
      "245/246 [============================>.] - ETA: 1s - loss: 0.5622 - accuracy: 0.8036\n",
      "Epoch 14: saving model to models\\acusticEvents-0014.ckpt\n",
      "246/246 [==============================] - 397s 2s/step - loss: 0.5618 - accuracy: 0.8038 - val_loss: 1.1306 - val_accuracy: 0.6791\n",
      "Epoch 15/100\n",
      "245/246 [============================>.] - ETA: 1s - loss: 0.5382 - accuracy: 0.8156\n",
      "Epoch 15: saving model to models\\acusticEvents-0015.ckpt\n",
      "246/246 [==============================] - 410s 2s/step - loss: 0.5372 - accuracy: 0.8160 - val_loss: 1.1293 - val_accuracy: 0.7127\n",
      "Epoch 16/100\n",
      "245/246 [============================>.] - ETA: 1s - loss: 0.4997 - accuracy: 0.8307\n",
      "Epoch 16: saving model to models\\acusticEvents-0016.ckpt\n",
      "246/246 [==============================] - 412s 2s/step - loss: 0.5013 - accuracy: 0.8303 - val_loss: 1.0089 - val_accuracy: 0.7151\n",
      "Epoch 17/100\n",
      "245/246 [============================>.] - ETA: 1s - loss: 0.4805 - accuracy: 0.8355\n",
      "Epoch 17: saving model to models\\acusticEvents-0017.ckpt\n",
      "246/246 [==============================] - 405s 2s/step - loss: 0.4811 - accuracy: 0.8356 - val_loss: 1.1647 - val_accuracy: 0.6803\n",
      "Epoch 18/100\n",
      "245/246 [============================>.] - ETA: 1s - loss: 0.4460 - accuracy: 0.8475\n",
      "Epoch 18: saving model to models\\acusticEvents-0018.ckpt\n",
      "246/246 [==============================] - 403s 2s/step - loss: 0.4495 - accuracy: 0.8461 - val_loss: 1.1459 - val_accuracy: 0.7091\n",
      "Epoch 19/100\n",
      "245/246 [============================>.] - ETA: 1s - loss: 0.4935 - accuracy: 0.8309\n",
      "Epoch 19: saving model to models\\acusticEvents-0019.ckpt\n",
      "246/246 [==============================] - 404s 2s/step - loss: 0.4932 - accuracy: 0.8311 - val_loss: 1.1844 - val_accuracy: 0.7091\n",
      "Epoch 20/100\n",
      "245/246 [============================>.] - ETA: 1s - loss: 0.4685 - accuracy: 0.8436\n",
      "Epoch 20: saving model to models\\acusticEvents-0020.ckpt\n",
      "246/246 [==============================] - 408s 2s/step - loss: 0.4685 - accuracy: 0.8437 - val_loss: 1.2960 - val_accuracy: 0.6719\n",
      "Epoch 21/100\n",
      "245/246 [============================>.] - ETA: 1s - loss: 0.5016 - accuracy: 0.8217\n",
      "Epoch 21: saving model to models\\acusticEvents-0021.ckpt\n",
      "246/246 [==============================] - 403s 2s/step - loss: 0.5011 - accuracy: 0.8220 - val_loss: 1.3316 - val_accuracy: 0.6659\n",
      "Epoch 22/100\n",
      "245/246 [============================>.] - ETA: 1s - loss: 0.4380 - accuracy: 0.8483\n",
      "Epoch 22: saving model to models\\acusticEvents-0022.ckpt\n",
      "246/246 [==============================] - 407s 2s/step - loss: 0.4376 - accuracy: 0.8481 - val_loss: 1.1280 - val_accuracy: 0.7031\n",
      "Epoch 23/100\n",
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      "Epoch 23: saving model to models\\acusticEvents-0023.ckpt\n",
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      "Epoch 48/100\n",
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      "Epoch 49/100\n",
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      "Epoch 50/100\n",
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      "Epoch 51/100\n",
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      "246/246 [==============================] - 402s 2s/step - loss: 0.3095 - accuracy: 0.8970 - val_loss: 1.7557 - val_accuracy: 0.6755\n",
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      "246/246 [==============================] - 402s 2s/step - loss: 0.3193 - accuracy: 0.8963 - val_loss: 1.2749 - val_accuracy: 0.6971\n",
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      "246/246 [==============================] - 404s 2s/step - loss: 0.2876 - accuracy: 0.9049 - val_loss: 1.3426 - val_accuracy: 0.6791\n",
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      "Epoch 71/100\n",
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      "246/246 [==============================] - 400s 2s/step - loss: 0.2907 - accuracy: 0.8994 - val_loss: 1.3270 - val_accuracy: 0.7043\n",
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      "246/246 [==============================] - 400s 2s/step - loss: 0.2842 - accuracy: 0.9054 - val_loss: 1.3976 - val_accuracy: 0.7079\n",
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      "246/246 [==============================] - 404s 2s/step - loss: 0.2869 - accuracy: 0.9002 - val_loss: 1.4271 - val_accuracy: 0.6851\n",
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      "246/246 [==============================] - 404s 2s/step - loss: 0.2916 - accuracy: 0.9026 - val_loss: 1.1416 - val_accuracy: 0.7260\n",
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      "246/246 [==============================] - 402s 2s/step - loss: 0.2944 - accuracy: 0.9004 - val_loss: 1.3139 - val_accuracy: 0.7296\n",
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      "246/246 [==============================] - 402s 2s/step - loss: 0.3000 - accuracy: 0.8974 - val_loss: 1.2497 - val_accuracy: 0.7284\n",
      "Epoch 78/100\n",
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      "246/246 [==============================] - 407s 2s/step - loss: 0.2818 - accuracy: 0.8991 - val_loss: 1.1218 - val_accuracy: 0.7308\n",
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      "246/246 [==============================] - 406s 2s/step - loss: 0.2742 - accuracy: 0.9077 - val_loss: 1.4007 - val_accuracy: 0.6839\n",
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      "Epoch 81/100\n",
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      "246/246 [==============================] - 405s 2s/step - loss: 0.2604 - accuracy: 0.9175 - val_loss: 1.3320 - val_accuracy: 0.7043\n",
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      "246/246 [==============================] - 403s 2s/step - loss: 0.3097 - accuracy: 0.8942 - val_loss: 1.2131 - val_accuracy: 0.7091\n",
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      "246/246 [==============================] - 407s 2s/step - loss: 0.2742 - accuracy: 0.9073 - val_loss: 1.4796 - val_accuracy: 0.7103\n",
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      "246/246 [==============================] - 403s 2s/step - loss: 0.2652 - accuracy: 0.9119 - val_loss: 1.2748 - val_accuracy: 0.7139\n",
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      "246/246 [==============================] - 404s 2s/step - loss: 0.2989 - accuracy: 0.8980 - val_loss: 1.2792 - val_accuracy: 0.6983\n",
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      "246/246 [==============================] - 403s 2s/step - loss: 0.3531 - accuracy: 0.8874 - val_loss: 1.2368 - val_accuracy: 0.7200\n",
      "Epoch 87/100\n",
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      "246/246 [==============================] - 408s 2s/step - loss: 0.3145 - accuracy: 0.8956 - val_loss: 1.4366 - val_accuracy: 0.6707\n",
      "Epoch 88/100\n",
      "245/246 [============================>.] - ETA: 1s - loss: 0.2666 - accuracy: 0.9125\n",
      "Epoch 88: saving model to models\\acusticEvents-0088.ckpt\n",
      "246/246 [==============================] - 406s 2s/step - loss: 0.2663 - accuracy: 0.9128 - val_loss: 1.3925 - val_accuracy: 0.7200\n",
      "Epoch 89/100\n",
      "245/246 [============================>.] - ETA: 1s - loss: 0.2711 - accuracy: 0.9111\n",
      "Epoch 89: saving model to models\\acusticEvents-0089.ckpt\n",
      "246/246 [==============================] - 411s 2s/step - loss: 0.2712 - accuracy: 0.9113 - val_loss: 1.1369 - val_accuracy: 0.7308\n",
      "Epoch 90/100\n",
      "245/246 [============================>.] - ETA: 1s - loss: 0.3012 - accuracy: 0.9035\n",
      "Epoch 90: saving model to models\\acusticEvents-0090.ckpt\n",
      "246/246 [==============================] - 415s 2s/step - loss: 0.3011 - accuracy: 0.9033 - val_loss: 1.1709 - val_accuracy: 0.7368\n",
      "Epoch 91/100\n",
      "245/246 [============================>.] - ETA: 1s - loss: 0.2847 - accuracy: 0.9041\n",
      "Epoch 91: saving model to models\\acusticEvents-0091.ckpt\n",
      "246/246 [==============================] - 413s 2s/step - loss: 0.2838 - accuracy: 0.9044 - val_loss: 1.2145 - val_accuracy: 0.7019\n",
      "Epoch 92/100\n",
      "245/246 [============================>.] - ETA: 1s - loss: 0.3241 - accuracy: 0.9027\n",
      "Epoch 92: saving model to models\\acusticEvents-0092.ckpt\n",
      "246/246 [==============================] - 418s 2s/step - loss: 0.3229 - accuracy: 0.9030 - val_loss: 1.6943 - val_accuracy: 0.6046\n",
      "Epoch 93/100\n",
      "245/246 [============================>.] - ETA: 1s - loss: 0.4247 - accuracy: 0.8640\n",
      "Epoch 93: saving model to models\\acusticEvents-0093.ckpt\n",
      "246/246 [==============================] - 416s 2s/step - loss: 0.4241 - accuracy: 0.8641 - val_loss: 1.3650 - val_accuracy: 0.7031\n",
      "Epoch 94/100\n",
      "245/246 [============================>.] - ETA: 1s - loss: 0.2850 - accuracy: 0.9055\n",
      "Epoch 94: saving model to models\\acusticEvents-0094.ckpt\n",
      "246/246 [==============================] - 420s 2s/step - loss: 0.2859 - accuracy: 0.9051 - val_loss: 1.1138 - val_accuracy: 0.7260\n",
      "Epoch 95/100\n",
      "245/246 [============================>.] - ETA: 1s - loss: 0.2430 - accuracy: 0.9164\n",
      "Epoch 95: saving model to models\\acusticEvents-0095.ckpt\n",
      "246/246 [==============================] - 421s 2s/step - loss: 0.2433 - accuracy: 0.9163 - val_loss: 1.1800 - val_accuracy: 0.7308\n",
      "Epoch 96/100\n",
      "245/246 [============================>.] - ETA: 1s - loss: 0.2871 - accuracy: 0.9069\n",
      "Epoch 96: saving model to models\\acusticEvents-0096.ckpt\n",
      "246/246 [==============================] - 427s 2s/step - loss: 0.2867 - accuracy: 0.9070 - val_loss: 1.1184 - val_accuracy: 0.7368\n",
      "Epoch 97/100\n",
      "245/246 [============================>.] - ETA: 1s - loss: 0.2428 - accuracy: 0.9155\n",
      "Epoch 97: saving model to models\\acusticEvents-0097.ckpt\n",
      "246/246 [==============================] - 413s 2s/step - loss: 0.2444 - accuracy: 0.9151 - val_loss: 1.2808 - val_accuracy: 0.7308\n",
      "Epoch 98/100\n",
      "245/246 [============================>.] - ETA: 1s - loss: 0.2545 - accuracy: 0.9148\n",
      "Epoch 98: saving model to models\\acusticEvents-0098.ckpt\n",
      "246/246 [==============================] - 410s 2s/step - loss: 0.2535 - accuracy: 0.9152 - val_loss: 1.4047 - val_accuracy: 0.7224\n",
      "Epoch 99/100\n",
      "245/246 [============================>.] - ETA: 1s - loss: 0.2558 - accuracy: 0.9213\n",
      "Epoch 99: saving model to models\\acusticEvents-0099.ckpt\n",
      "246/246 [==============================] - 410s 2s/step - loss: 0.2572 - accuracy: 0.9205 - val_loss: 1.4348 - val_accuracy: 0.6959\n",
      "Epoch 100/100\n",
      "245/246 [============================>.] - ETA: 1s - loss: 0.2711 - accuracy: 0.9128\n",
      "Epoch 100: saving model to models\\acusticEvents-0100.ckpt\n",
      "246/246 [==============================] - 411s 2s/step - loss: 0.2703 - accuracy: 0.9131 - val_loss: 1.3283 - val_accuracy: 0.7440\n"
     ]
    }
   ],
   "source": [
    "model.load_weights('models/acusticEvents-0040.ckpt')\n",
    "history = model.fit(train_dataset,\n",
    "          validation_data=valid_dataset,\n",
    "          validation_steps=len(valid_df)//BATCH_SIZE,\n",
    "          steps_per_epoch=len(train_df)//BATCH_SIZE,\n",
    "          epochs=100,\n",
    "          callbacks=[model_save_callback])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Evalution"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Accuracy\n",
    "As we can see our best accuracy is 75,5% on validation dataset that we achieved in epoch 43. Which is not bad considering complexity of the dataset.\n",
    "On training data we had about 86% in same epoch. From that point model improved up to 92% on training dataset, but did not improved a bit on validation dataset. So it clearly started to overfit on training data.\n",
    "I also tried different models and augmenation settings but result were pretty much always under 80%. So in order to improve accuracy I would suggest to use some verified models and add/modify features such as mfcc."
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "results = []\n",
    "for i in range(1,100):\n",
    "    model.load_weights(f'models/acusticEvents-{i:04d}.ckpt')\n",
    "    print(f\"Model-epoch:{i:04d}\")\n",
    "    temp_result = model.evaluate(valid_dataset)\n",
    "    results.append(temp_result)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": true
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best model result is 0.7550776600837708 in epoch 43\n"
     ]
    }
   ],
   "source": [
    "res = np.array(results)\n",
    "print(f\"Best model result is {np.max(res[:,1])} in epoch {np.argmax(res[:,1]) + 1}\")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Confusion matrix\n",
    "From the Confusion matrix we can see following:\n",
    "- Every gun shot was accurately recognized.\n",
    "- In 21% it predicted 'street music' as 'car horn', which did not surprise me because some recordings of 'car horn' contains the sound of the street (including music)\n",
    "- On the other hand accuracy of siren is pretty stunning to me because I supposed that this one will be easiest to recognize alongside with gun shot.\n",
    "- In almost 30% it identifies siren as 'children playing' and in 13% it is identified as 'drilling'."
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "27/27 [==============================] - 2s 55ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": "[Text(0.5, 0, 'Predicted'), Text(0, 0.5, 'True')]"
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 1080x1080 with 2 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
    "\n",
    "model.load_weights(f'models/acusticEvents-0043.ckpt')\n",
    "true_categories = valid_df[\"classID\"]\n",
    "cat_labels = valid_df.sort_values(\"classID\")[\"class\"].unique()\n",
    "#Predict\n",
    "y_prediction = model.predict(valid_dataset)\n",
    "predicted_categories = tf.argmax(y_prediction, axis=1)\n",
    "\n",
    "#Create confusion matrix and normalizes it over predicted (columns)\n",
    "fig, ax = plt.subplots(figsize=(15,15))\n",
    "cm = confusion_matrix(true_categories, predicted_categories , normalize='true')\n",
    "cmd = ConfusionMatrixDisplay(cm, display_labels=cat_labels)\n",
    "cmd.plot(ax=ax, cmap=\"magma\")\n",
    "cmd.ax_.set(xlabel='Predicted', ylabel='True')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}