TagAudio.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Train.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/QColeman97/AudioTagger/blob/master/TagAudio.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tcgupmmSzS9x",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 88
        },
        "outputId": "bd792fb3-6dfe-4827-b5f4-7cd489aab900"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "# import seaborn as sns # for data visualization\n",
        "\n",
        "# import IPython\n",
        "# import IPython.display as ipd #To play sound in notebook\n",
        "# import scipy as sci\n",
        "# import wave\n",
        "from pathlib import Path\n",
        "\n",
        "from scipy.fftpack import fft #Fast Fourier Transformation\n",
        "from scipy.io import wavfile\n",
        "\n",
        "import librosa\n",
        "from librosa import display\n",
        "import os\n",
        "import glob\n",
        "\n",
        "input_path = 'drive/My Drive/AudioTaggerData/'\n",
        "\n",
        "\n",
        "# input_path = '../AudioTaggerData/'\n",
        "train_files_path = input_path + 'FSDKaggle2018.audio_train'\n",
        "test_files_path = input_path + 'FSDKaggle2018.audio_test'\n",
        "train_csv_path = (input_path +\n",
        "                  'FSDKaggle2018.meta/train_post_competition.csv')\n",
        "test_csv_path = (input_path +\n",
        "                 'FSDKaggle2018.meta/' +\n",
        "                 'test_post_competition_scoring_clips.csv')\n",
        "\n",
        "#scipy.wavfile.read returns rate of wave, and # of data read\n",
        "filename = '/001ca53d.wav'\n",
        "# sample_rate, samples = wavfile.read(str(audio_train_file) + filename)\n",
        "# print(samples)\n",
        "# print(train.shape)\n",
        "\n",
        "# Data preprocessing part\n",
        "\n",
        "df_train = pd.read_csv(train_csv_path)\n",
        "df_test = pd.read_csv(test_csv_path)\n",
        "\n",
        "unique_labels = df_train.label.unique()\n",
        "num_class = len(unique_labels)\n",
        "label2index = {label: index for index, label in enumerate(unique_labels)}\n",
        "\n",
        "# print('Label to index:')\n",
        "# print(label2index)\n",
        "\n",
        "train_dict = pd.Series.from_csv(train_csv_path, header = 0).to_dict()\n",
        "test_dict = pd.Series.from_csv(test_csv_path, header = 0).to_dict()\n",
        "\n",
        "\n",
        "# print('Train dict at:')\n",
        "# print(test_dict['40f595b9.wav'])\n",
        "# KeyError: '40f595b9.wav'\n",
        "\n",
        "# train_df = pd.read_csv(train_csv_path, header = 0)\n",
        "# train_dict = train_df.transpose().to_dict()\n",
        "# print('train dict:')\n",
        "# print(train_dict)\n",
        "\n",
        "#Indices of manually verified training data\n",
        "# verified_train = np.array(df_train[df_train.manually_verified == 1].index)\n",
        "# print(len(verified_train))\n",
        "# print(len(df_train))\n",
        "\n",
        "#array of labels in number form (0 = hi-hat, 1 = saxophone, etc)\n",
        "label_emb_indices = np.array([label2index[label] for label in df_train.label])\n",
        "\n",
        "label_emb_test_indices = np.array([label2index[label] for label in df_test.label])\n",
        "\n",
        "# print(plain_y_train)\n",
        "\n",
        "# print('Label emb indices:')\n",
        "# print(label_emb_indices)\n",
        "\n",
        "# # Approach X uses longer sound, then it uses suppressed\n",
        "# # confX['sampling_rate'] = 26000\n",
        "# # sampling_rate = 44100 # Original file sr\n",
        "# sampling_rate = 32000\n",
        "# # duration = 4\n",
        "# duration = 5\n",
        "# # confX['hop_length'] = 520  # 20ms\n",
        "# hop_length = 192\n",
        "# fmin = 20\n",
        "# fmax = sampling_rate // 2\n",
        "# # confX['n_mels'] = 48\n",
        "# n_mels = 128\n",
        "# # confX['n_fft'] = confX['n_mels'] * 20\n",
        "# n_fft = 1024\n",
        "# audio_split = 'dont_crop'\n",
        "# samples = sampling_rate * duration\n",
        "# dims = (n_mels, 1 + int(np.floor(samples / hop_length)), 1)\n",
        "\n",
        "\n",
        "def pre_process(pathname):\n",
        "    sampling_rate = 32000\n",
        "    # duration = 4\n",
        "    # duration = 5\n",
        "    # confX['hop_length'] = 520  # 20ms\n",
        "    hop_length = 192\n",
        "    # fmin = 20\n",
        "    # fmax = sampling_rate // 2\n",
        "    fmax = None\n",
        "    # confX['n_mels'] = 48\n",
        "    n_mels = 128 # originally\n",
        "#     n_mels = 32\n",
        "    # confX['n_fft'] = confX['n_mels'] * 20\n",
        "    n_fft = 1024\n",
        "    # audio_split = 'dont_crop'\n",
        "    # samples = sampling_rate * duration\n",
        "    # dims = (n_mels, 1 + int(np.floor(samples / hop_length)), 1)\n",
        "\n",
        "    # TODO: Correct spectrogram creation to minimize bad data\n",
        "\n",
        "    # y, sr = librosa.load(pathname, sr = None)\n",
        "    y, sr = librosa.load(pathname, sr = sampling_rate)\n",
        "    # print('Y len:', len(y))\n",
        "    y, (trim_begin, trim_end) = librosa.effects.trim(y)\n",
        "\n",
        "    # print('Y len:', len(y))\n",
        "#     y = librosa.effects.time_stretch(y, 2)\n",
        "#     print('Y len:', len(y))\n",
        "\n",
        "    # while len(y) > 10000:\n",
        "    #     y = librosa.effects.time_stretch(y, 2)\n",
        "    #     print('Y len:', len(y))\n",
        "    # print()\n",
        "\n",
        "    # Amplitudes of STFT\n",
        "    stft = np.abs(librosa.stft(y, n_fft = n_fft, hop_length = hop_length,\n",
        "                               window = 'hann', center = True,\n",
        "                               pad_mode = 'reflect'))\n",
        "\n",
        "    # print('stft shape:', stft.shape)\n",
        "\n",
        "    freqs = librosa.core.fft_frequencies(sr = sampling_rate, n_fft = n_fft)\n",
        "    stft = librosa.perceptual_weighting(stft*2, freqs, ref = 1.0, amin = 1e-10,\n",
        "                                        top_db = 99.0)\n",
        "\n",
        "    # print('stft shape:', stft.shape)\n",
        "\n",
        "    # Apply mel filterbank\n",
        "    # Power param is set to 2 (power) by default\n",
        "    mel_spect = librosa.feature.melspectrogram(S = stft, sr = sampling_rate,\n",
        "                                               n_mels = n_mels, fmax = fmax)\n",
        "\n",
        "    # print('mel shape:', mel_spect.shape)\n",
        "\n",
        "    log_mel_spect = librosa.core.power_to_db(mel_spect)\n",
        "\n",
        "    # print('log mel shape:', log_mel_spect.shape)\n",
        "\n",
        "    # spectrogram = librosa.feature.melspectrogram(S = stft)\n",
        "    # Keep spectrogram\n",
        "    # return np.asarray(spectrogram)\n",
        "    return np.asarray(log_mel_spect)\n",
        "\n",
        "\n",
        "# pre_process(audio_train_file + filename)\n",
        "def get_data(pathname, training = True):\n",
        "    file_list = glob.glob(os.path.join(pathname, '*.wav'))\n",
        "\n",
        "    if training:\n",
        "        data_f = open('Audio.train', 'w')\n",
        "    else:\n",
        "        data_f = open('Audio.test', 'w')\n",
        "\n",
        "    # print(file_list)\n",
        "#     spectrograms, times = [], []\n",
        "    spectrograms = np.ndarray((9474, 256, 128))\n",
        "\n",
        "    for i, file in enumerate(file_list):\n",
        "        print(\"%04d / %d | %s\" % (i + 1, len(file_list), file))\n",
        "\n",
        "        try:\n",
        "            spectrogram = pre_process(file)\n",
        "        except Exception:\n",
        "            print('Weird, couldnt convert to spectrogram, skipping file')\n",
        "            continue\n",
        "\n",
        "        # times.append(spectrogram.shape[1])\n",
        "\n",
        "        # Originally 2000 (average)\n",
        "#         time_restriction = 64\n",
        "        time_restriction = 256\n",
        "        if time_restriction >= spectrogram.shape[1]:\n",
        "            pad_amount = time_restriction - spectrogram.shape[1]\n",
        "            # Use avg or max time\n",
        "            spectrogram = np.pad(spectrogram, ((0, 0), (0, pad_amount)),\n",
        "                                 'minimum')\n",
        "        else:\n",
        "            spectrogram = spectrogram[:, :time_restriction]\n",
        "\n",
        "        spectrogram = spectrogram.transpose()\n",
        "\n",
        "        # print(\"Spectrogram Shape:\", spectrogram.shape)\n",
        "\n",
        "#         spectrograms.append(spectrogram.astype(np.float32))\n",
        "        for j in range(len(spectrogram)):\n",
        "            for k in range(len(spectrogram[j])):\n",
        "                spectrograms[i][j][k] = spectrogram[j][k].astype(np.float32)\n",
        "                \n",
        "        # data_f.write(np.array2string(spectrogram) + '\\n\\n')\n",
        "#         np.savetxt(data_f, spectrogram)\n",
        "#         data_f.write('\\n')\n",
        "\n",
        "        # if i > 500:\n",
        "        #     break\n",
        "        # if 32 < i < 50:  # 34 is a weird one\n",
        "#         if i % 12 == 0:\n",
        "        \n",
        "#             plt.figure(\"General-Purpose \")\n",
        "#             plt.clf()\n",
        "#             plt.subplots_adjust(right = 0.98, left = 0.1, bottom = 0.1,\n",
        "#                                 top = 0.99)\n",
        "#             plt.imshow(spectrogram, origin = \"lower\",\n",
        "#                        interpolation = \"nearest\", cmap = \"viridis\")\n",
        "#             plt.xlabel(\"%d bins\" % spectrogram.shape[1])\n",
        "#             plt.ylabel(\"%d frames\" % spectrogram.shape[0])\n",
        "#             plt.colorbar()\n",
        "#             plt.show()\n",
        "        \n",
        "            # display.specshow(spectrogram, y_axis = 'log', x_axis = 'time')\n",
        "            #\n",
        "            # plt.title('Mel Spectrogram')\n",
        "            # plt.colorbar(format = '%+2.0f dB')\n",
        "            # plt.tight_layout()\n",
        "            # plt.show()\n",
        "        \n",
        "            # print('Spectrogram:', i)\n",
        "            # print(spectrogram)\n",
        "\n",
        "    # average_time = np.average(times)\n",
        "    # print('Average timesteps:', average_time)\n",
        "    # max_time = np.amax(times)\n",
        "    # print('Max timesteps:', max_time)\n",
        "\n",
        "    return spectrograms\n",
        "#     data_f.close()\n",
        "\n",
        "\n",
        "def get_labels(pathname, training = True):\n",
        "    file_list = glob.glob(os.path.join(pathname, '*.wav'))\n",
        "\n",
        "    if training:\n",
        "        labels_f = open('Labels.train', 'w')\n",
        "    else:\n",
        "        labels_f = open('Labels.test', 'w')\n",
        "\n",
        "#     labels = []\n",
        "    labels = np.ndarray((1570, 41))\n",
        "    for i, file in enumerate(file_list):\n",
        "#         label = np.zeros((41,))\n",
        "        categ = (train_dict[file.split('/')[-1]] if\n",
        "            (training) else test_dict[file.split('/')[-1]])\n",
        "        hot_index = label2index[categ]\n",
        "        labels[i][hot_index] = 1\n",
        "#         labels.append(label)\n",
        "#         labels_f.write(' '.join([str(x) for x in label]) + '\\n')\n",
        "\n",
        "    return np.array(labels)\n",
        "#     labels_f.close()\n",
        "\n",
        "\n",
        "# get_data(train_files_path, training = True)\n",
        "# get_labels(train_files_path, training = True)\n"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:4141: FutureWarning: from_csv is deprecated. Please use read_csv(...) instead. Note that some of the default arguments are different, so please refer to the documentation for from_csv when changing your function calls\n",
            "  infer_datetime_format=infer_datetime_format)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QbbUGp4yGKIt",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 66864
        },
        "outputId": "b6f21687-0124-476e-968b-0ccbc976eac5"
      },
      "source": [
        "from keras import models, layers\n",
        "from keras_preprocessing.image import ImageDataGenerator\n",
        "# import librosa\n",
        "# from librosa import display\n",
        "import numpy as np\n",
        "# import matplotlib.pyplot as plt\n",
        "# import IPython.display as ipd  # only for IPython notebooks\n",
        "# import pyaudio\n",
        "# import wave\n",
        "# import PreProcess\n",
        "\n",
        "\n",
        "# #1 preprocess method\n",
        "# Normalize input w/ batch normalization (BN)\n",
        "# Input is trnasformed into logmel domain (turned into log-mel spectrograms)\n",
        "# BN again & reshaped (time, frequency, 1) - greyscale image\n",
        "# Then conv stuff\n",
        "\n",
        "# Hyperparams / config to test:\n",
        "# ch 7: (advanced)\n",
        "#   model ensembling (of 2D CNN & Combined 1D CNN & RNN combo, etc??)\n",
        "#   Replace Conv2D w/ SeparableConv2D\n",
        "#   Batch normailization (can replace regularization)\n",
        "#   Inception\n",
        "#   Residual connections\n",
        "#   DenseNet is a good model to follow (top leader) or fine tune from\n",
        "\n",
        "# ch 5: (convnets)\n",
        "#   Tune HP (# neurons, layers, epochs, batch_size)\n",
        "#   dropout, regularization (can be replaced by BN)\n",
        "#   Data augmentation\n",
        "\n",
        "# K-Fold Validation\n",
        "\n",
        "\n",
        "# class LyrParams:\n",
        "#     # Describes, for a layer, # channels/neurons, dropout rate &\n",
        "#     # regularization choice (Lyr = layer)\n",
        "#     def __init__(self, units, dpt = None):\n",
        "#         self.units = units\n",
        "#         self.dpt = dpt\n",
        "\n",
        "# nn.add(layers.Conv2D(prm_lyrs[1].units, (3, 3), activation = 'relu'))\n",
        "# if prm_lyrs[0].dpt:\n",
        "#   nn.add(layers.dpt(prm_lyrs[0].dpt))\n",
        "\n",
        "# Lab 3 is a good one for backup\n",
        "# Model structure after best methods - 6 conv units (conv *2 + maxpool)\n",
        "def make_model(input_shape):\n",
        "    # Current Shape: (500, 128, 1)  # 500 = timesteps, 128 = frequencies\n",
        "    nn = models.Sequential()\n",
        "    nn.add(layers.SeparableConv2D(64, (3, 3), padding = 'same',\n",
        "                                  activation = 'relu',\n",
        "                                  input_shape = input_shape))\n",
        "    # Shape: (126, 498, 64)\n",
        "    nn.add(layers.BatchNormalization())\n",
        "    nn.add(layers.SeparableConv2D(64, (3, 3), padding = 'same',\n",
        "                                  activation = 'relu'))\n",
        "    # Shape: (124, 496, 64)\n",
        "    nn.add(layers.BatchNormalization())\n",
        "    nn.add(layers.MaxPooling2D((2, 2)))\n",
        "    # Shape: (62, 248, 64)\n",
        "    nn.add(layers.Dropout(0.3))\n",
        "\n",
        "    nn.add(layers.SeparableConv2D(128, (3, 3), padding = 'same',\n",
        "                                  activation = 'relu'))\n",
        "    # Shape: (60, 246, 128)\n",
        "    nn.add(layers.BatchNormalization())\n",
        "    nn.add(layers.SeparableConv2D(128, (3, 3), padding = 'same',\n",
        "                                  activation = 'relu'))\n",
        "    # Shape: (58, 244, 128)\n",
        "    nn.add(layers.BatchNormalization())\n",
        "    nn.add(layers.MaxPooling2D((2, 2)))\n",
        "    # Shape: (29, 122, 128)\n",
        "    nn.add(layers.Dropout(0.3))\n",
        "\n",
        "#     # Possibly make this block more like the others\n",
        "#     nn.add(layers.SeparableConv2D(256, (3, 3), padding = 'same',\n",
        "#                                   activation = 'relu'))\n",
        "#     # Shape: (27, 120, 256)\n",
        "#     nn.add(layers.BatchNormalization())\n",
        "#     nn.add(layers.Dropout(0.3))\n",
        "    nn.add(layers.SeparableConv2D(256, (3, 3), padding = 'same',\n",
        "                                  activation = 'relu'))\n",
        "    # Shape: (25, 118, 256)\n",
        "    nn.add(layers.BatchNormalization())\n",
        "    nn.add(layers.Dropout(0.3))\n",
        "    nn.add(layers.SeparableConv2D(256, (3, 3), padding = 'same',\n",
        "                                  activation = 'relu'))\n",
        "    # Shape: (23, 116, 256)\n",
        "    nn.add(layers.BatchNormalization())\n",
        "    nn.add(layers.Dropout(0.3))\n",
        "    nn.add(layers.SeparableConv2D(256, (3, 3), padding = 'same',\n",
        "                                  activation = 'relu'))\n",
        "    # Shape: (21, 114, 256)\n",
        "    nn.add(layers.BatchNormalization())\n",
        "    nn.add(layers.MaxPooling2D((2, 2)))\n",
        "    # Shape: (10, 57, 256)\n",
        "    nn.add(layers.Dropout(0.3))\n",
        "\n",
        "#     nn.add(layers.SeparableConv2D(512, (3, 3), padding = 'same',\n",
        "#                                   activation = 'relu'))\n",
        "#     # Shape: (8, 55, 512)\n",
        "#     nn.add(layers.BatchNormalization())\n",
        "#     nn.add(layers.SeparableConv2D(512, (3, 3), padding = 'same',\n",
        "#                                   activation = 'relu'))\n",
        "#     # Shape: (6, 53, 512)\n",
        "#     nn.add(layers.BatchNormalization())\n",
        "#     nn.add(layers.MaxPooling2D((2, 2)))\n",
        "#     # Shape: (3, 26, 512)\n",
        "#     nn.add(layers.Dropout(0.3))\n",
        "\n",
        "    nn.add(layers.SeparableConv2D(512, (3, 3), padding = 'same',\n",
        "                                  activation = 'relu'))\n",
        "    # Shape: (1, 24, 512)\n",
        "    nn.add(layers.BatchNormalization())\n",
        "    nn.add(layers.SeparableConv2D(512, (3, 3), padding = 'same',\n",
        "                                  activation = 'relu'))\n",
        "    nn.add(layers.BatchNormalization())\n",
        "    nn.add(layers.GlobalAveragePooling2D())\n",
        "\n",
        "    nn.add(layers.Dense(41, activation = 'softmax'))\n",
        "    return nn\n",
        "\n",
        "\n",
        "# Get data\n",
        "# input_path = 'AudioTaggerData/'\n",
        "input_path = 'drive/My Drive/AudioTaggerData/'\n",
        "\n",
        "train_files_path = input_path + 'FSDKaggle2018.audio_train'\n",
        "test_files_path = input_path + 'FSDKaggle2018.audio_test'\n",
        "train_csv_path = (input_path +\n",
        "                  'FSDKaggle2018.meta/train_post_competition.csv')\n",
        "test_csv_path = (input_path +\n",
        "                 'FSDKaggle2018.meta/' +\n",
        "                 'test_post_competition_scoring_clips.csv')\n",
        "\n",
        "\n",
        "def get_train_data():\n",
        "#     data = np.ndarray((9474, 64, 32))\n",
        "    data = np.ndarray((9474, 256, 128))\n",
        "\n",
        "#     data_len, data = 9474, []\n",
        "    data_len, time_len, freq = 9474, 256, 128\n",
        "\n",
        "#     data_len, data = 3, []\n",
        "    with open(input_path + 'Audio.train', 'r') as data_f:\n",
        "        for i in range(data_len):\n",
        "#             sample = []\n",
        "#             time_step = [float(elem) for elem in\n",
        "#                          data_f.readline().split()]\n",
        "#             print(time_step)\n",
        "            for j in range(time_len):\n",
        "#             while time_step != []:\n",
        "#                 sample.append(np.array(time_step))\n",
        "                \n",
        "    \n",
        "                time_step = [float(elem) for elem in\n",
        "                             data_f.readline().split()]\n",
        "#                 sample.append(time_step)\n",
        "                \n",
        "                if len(time_step) < freq:\n",
        "                    rest = freq - len(time_step)\n",
        "                    time_step += [-100.0 for x in range(rest)]\n",
        "    \n",
        "                for k in range(freq):\n",
        "#                     if (len(time_step) != 32):\n",
        "#                         print('BAD time step len:', i)\n",
        "#                     if (i >= 9474 or j >= 64 or k >= 32):\n",
        "#                         print('OOB error:')\n",
        "#                         print(i,j,k)\n",
        "#                     print(len(time_step))\n",
        "                    data[i][j][k] = time_step[k]\n",
        "            \n",
        "#                 print(time_step)\n",
        "                # print('line')\n",
        "                # print(line)\n",
        "#                 sample.append(np.array(time_step))\n",
        "#             data.append(np.array(sample))\n",
        "#             data.append(sample)\n",
        "\n",
        "            data_f.readline()\n",
        "\n",
        "#             print('DATA:')\n",
        "#             print(data)\n",
        "\n",
        "#     return np.array(data)\n",
        "    return data\n",
        "\n",
        "def get_test_data():\n",
        "    data = np.ndarray((1570, 256, 128))\n",
        "    data_len = 1570\n",
        "\n",
        "#     data_len, data = 3, []\n",
        "    with open(input_path + 'Audio.test', 'r') as data_f:\n",
        "        for i in range(data_len):\n",
        "\n",
        "            for j in range(256):\n",
        "    \n",
        "                time_step = [float(elem) for elem in\n",
        "                             data_f.readline().split()]\n",
        "#                 sample.append(time_step)\n",
        "                \n",
        "                if len(time_step) < 128:\n",
        "                    rest = 128 - len(time_step)\n",
        "                    time_step += [-100.0 for x in range(rest)]\n",
        "    \n",
        "                for k in range(128):\n",
        "                    data[i][j][k] = time_step[k]\n",
        "\n",
        "            data_f.readline()\n",
        "\n",
        "    return data\n",
        "\n",
        "\n",
        "def get_train_labels():\n",
        "    labels = np.ndarray((9474, 41))\n",
        "#     labels_len, labels = 9474, []\n",
        "    labels_len = 9474\n",
        "\n",
        "#     labels_len, labels = 3, []\n",
        "    with open(input_path + 'Labels.train', 'r') as label_f:\n",
        "        for i in range(labels_len):\n",
        "            label = [float(elem) for elem in label_f.readline().split()]\n",
        "#             labels.append(label)\n",
        "\n",
        "            if len(label) < 41:\n",
        "                    rest = 41 - len(label)\n",
        "                    label += [0.0 for x in range(rest)]\n",
        "            \n",
        "            for j in range(41):\n",
        "                labels[i][j] = label[j]\n",
        "\n",
        "#     return np.array(labels)\n",
        "    return labels\n",
        "\n",
        "def get_test_labels():\n",
        "    labels = np.ndarray((1570, 41))\n",
        "#     labels_len, labels = 9474, []\n",
        "    labels_len = 1570\n",
        "\n",
        "#     labels_len, labels = 3, []\n",
        "    with open(input_path + 'Labels.test', 'r') as label_f:\n",
        "        for i in range(labels_len):\n",
        "            label = [float(elem) for elem in label_f.readline().split()]\n",
        "#             labels.append(label)\n",
        "\n",
        "            if len(label) < 41:\n",
        "                    rest = 41 - len(label)\n",
        "                    label += [0.0 for x in range(rest)]\n",
        "            \n",
        "            for j in range(41):\n",
        "                labels[i][j] = label[j]\n",
        "\n",
        "#     return np.array(labels)\n",
        "    return labels\n",
        "\n",
        "# 1. Create training data (log mel spectrograms)\n",
        "# dummy_samples = 10\n",
        "# Num train samples = 9474\n",
        "# Num test samples = 1570\n",
        "\n",
        "\n",
        "\n",
        "# UNDO\n",
        "# train_data = get_train_data()\n",
        "# train_labels = get_train_labels()\n",
        "\n",
        "# test_data = get_test_data()\n",
        "# test_labels = get_test_labels()\n",
        "test_data = get_data(test_files_path, training = False)\n",
        "test_labels = get_labels(test_files_path, training = False)\n",
        "\n",
        "#UNDO\n",
        "# train_samples, test_samples = 9474, 1570\n",
        "# max_timesteps = train_data.shape[1]\n",
        "# num_freq = train_data.shape[2]\n"
      ],
      "execution_count": 0,
      "outputs": [
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    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T4ua6ZrXRNkt",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1788
        },
        "outputId": "5e2b157e-3bb8-4fb0-c2f7-3686b8e7a112"
      },
      "source": [
        "mean = train_data.mean(axis = 0)\n",
        "# print('Mean of sample 1:')\n",
        "# print(mean[0])\n",
        "train_data -= mean\n",
        "std = train_data.std(axis = 0)\n",
        "train_data /= std\n",
        "\n",
        "# ONCE I HAVE TEST DATA\n",
        "test_data -= mean\n",
        "test_data /= std\n",
        "\n",
        "# Use getter methods here\n",
        "# dummy_train_data = np.random.random((dummy_samples, dummy_max_timesteps,\n",
        "#                                      dummy_num_freq, 1))\n",
        "# # Categorical labels\n",
        "# dummy_train_labels = np.zeros((10, 41))\n",
        "# for i, label in enumerate(dummy_train_labels):\n",
        "#     label[i] = 1\n",
        "# print(dummy_train_labels)\n",
        "\n",
        "model = make_model((max_timesteps, num_freq, 1))\n",
        "model.compile(optimizer = 'rmsprop', loss = 'categorical_crossentropy',\n",
        "              metrics = ['accuracy'])\n",
        "\n",
        "k = 4\n",
        "num_val = len(train_data) // k\n",
        "num_train = len(train_labels) - num_val\n",
        "all_val_acc_histories, all_val_loss_histories = [], []\n",
        "for x in range(k):\n",
        "    val_data = train_data[x * num_val: (x + 1) * num_val]\n",
        "    val_labels = train_labels[x * num_val: (x + 1) * num_val]\n",
        "\n",
        "    partial_train_data = np.concatenate(\n",
        "        [train_data[: x * num_val], train_data[(x + 1) * num_val:]],\n",
        "        axis = 0)\n",
        "    partial_train_labels = np.concatenate(\n",
        "        [train_labels[: x * num_val],\n",
        "         train_labels[(x + 1) * num_val:]],\n",
        "        axis = 0)\n",
        "    \n",
        "    partial_train_data = partial_train_data.reshape(\n",
        "            (partial_train_data.shape[0], \n",
        "             partial_train_data.shape[1], \n",
        "             partial_train_data.shape[2], \n",
        "             1))\n",
        "    \n",
        "    val_data = val_data.reshape(\n",
        "            (val_data.shape[0], \n",
        "             val_data.shape[1], \n",
        "             val_data.shape[2], \n",
        "             1))\n",
        "    \n",
        "    hst = model.fit(partial_train_data, partial_train_labels, batch_size = 1024,\n",
        "                    epochs = 10, validation_data = (val_data, val_labels),)\n",
        "\n",
        "    hst = hst.history\n",
        "    all_val_loss_histories.append(hst['val_loss'])\n",
        "    all_val_acc_histories.append(hst['val_acc'])\n",
        "\n",
        "avg_val_loss_hst = np.mean(all_val_loss_histories, axis = 0)\n",
        "avg_val_acc_hst = np.mean(all_val_acc_histories, axis = 0)\n",
        "\n",
        "best_loss, best_acc, prev_acc, best_epoch = None, None, None, 0\n",
        "\n",
        "acc_increased = True\n",
        "for i in range(10):\n",
        "    print(avg_val_acc_hst[i], '/', avg_val_loss_hst[i])\n",
        "\n",
        "    if prev_acc is not None and avg_val_acc_hst[i] < prev_acc:\n",
        "        acc_increased = False\n",
        "    prev_acc = avg_val_loss_hst[i]\n",
        "\n",
        "    if (best_acc is None or avg_val_acc_hst[i] > best_acc and\n",
        "            acc_increased):\n",
        "        best_acc = avg_val_acc_hst[i]\n",
        "        best_loss = avg_val_loss_hst[i]\n",
        "        best_epoch = i + 1\n",
        "\n",
        "print('Best val loss:', best_loss, '& with acc:', best_acc, 'at epoch:',\n",
        "      str(best_epoch))\n",
        "\n",
        "model.save('2DCNN.h5')\n"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean of sample 1:\n",
            "[-86.62562035 -80.24301205 -76.96972696 -74.24042989 -72.61073198\n",
            " -72.02183324 -71.96739275 -71.94178303 -72.11115085 -72.31134021\n",
            " -72.43132402 -73.15902811 -73.39463413 -73.82801204 -74.54069714\n",
            " -75.03079402 -75.5396978  -76.29402385 -76.83100721 -77.34660071\n",
            " -77.61326341 -78.22247951 -78.70824379 -79.37451051 -80.45232453\n",
            " -81.33338928 -82.41725067 -83.25649102 -84.30028947 -85.23726603\n",
            " -86.02354532 -86.80838875]\n",
            "Train on 7106 samples, validate on 2368 samples\n",
            "Epoch 1/10\n",
            "7106/7106 [==============================] - 9s 1ms/step - loss: 3.4839 - acc: 0.0948 - val_loss: 3.5066 - val_acc: 0.1035\n",
            "Epoch 2/10\n",
            "7106/7106 [==============================] - 6s 838us/step - loss: 3.1340 - acc: 0.1714 - val_loss: 3.1900 - val_acc: 0.1660\n",
            "Epoch 3/10\n",
            "7106/7106 [==============================] - 6s 843us/step - loss: 2.9191 - acc: 0.2118 - val_loss: 3.1819 - val_acc: 0.1959\n",
            "Epoch 4/10\n",
            "7106/7106 [==============================] - 6s 832us/step - loss: 2.7378 - acc: 0.2518 - val_loss: 3.3186 - val_acc: 0.1795\n",
            "Epoch 5/10\n",
            "7106/7106 [==============================] - 6s 821us/step - loss: 2.6411 - acc: 0.2658 - val_loss: 2.9438 - val_acc: 0.2323\n",
            "Epoch 6/10\n",
            "7106/7106 [==============================] - 6s 812us/step - loss: 2.5337 - acc: 0.2999 - val_loss: 3.0453 - val_acc: 0.2264\n",
            "Epoch 7/10\n",
            "7106/7106 [==============================] - 6s 812us/step - loss: 2.4390 - acc: 0.3259 - val_loss: 2.8863 - val_acc: 0.2707\n",
            "Epoch 8/10\n",
            "7106/7106 [==============================] - 6s 807us/step - loss: 2.3846 - acc: 0.3314 - val_loss: 2.9793 - val_acc: 0.2606\n",
            "Epoch 9/10\n",
            "7106/7106 [==============================] - 6s 805us/step - loss: 2.3067 - acc: 0.3504 - val_loss: 2.8706 - val_acc: 0.2804\n",
            "Epoch 10/10\n",
            "7106/7106 [==============================] - 6s 801us/step - loss: 2.2810 - acc: 0.3642 - val_loss: 2.8905 - val_acc: 0.2880\n",
            "Train on 7106 samples, validate on 2368 samples\n",
            "Epoch 1/10\n",
            "7106/7106 [==============================] - 6s 800us/step - loss: 2.2808 - acc: 0.3645 - val_loss: 2.7032 - val_acc: 0.3243\n",
            "Epoch 2/10\n",
            "7106/7106 [==============================] - 6s 804us/step - loss: 2.2294 - acc: 0.3757 - val_loss: 2.6236 - val_acc: 0.3315\n",
            "Epoch 3/10\n",
            "7106/7106 [==============================] - 6s 807us/step - loss: 2.2129 - acc: 0.3708 - val_loss: 2.5732 - val_acc: 0.3454\n",
            "Epoch 4/10\n",
            "7106/7106 [==============================] - 6s 808us/step - loss: 2.1672 - acc: 0.3870 - val_loss: 2.5079 - val_acc: 0.3623\n",
            "Epoch 5/10\n",
            "7106/7106 [==============================] - 6s 809us/step - loss: 2.1071 - acc: 0.4035 - val_loss: 2.4059 - val_acc: 0.3801\n",
            "Epoch 6/10\n",
            "7106/7106 [==============================] - 6s 811us/step - loss: 2.0939 - acc: 0.4053 - val_loss: 2.3587 - val_acc: 0.3860\n",
            "Epoch 7/10\n",
            "7106/7106 [==============================] - 6s 811us/step - loss: 2.0675 - acc: 0.4095 - val_loss: 2.3993 - val_acc: 0.3788\n",
            "Epoch 8/10\n",
            "7106/7106 [==============================] - 6s 813us/step - loss: 2.0198 - acc: 0.4256 - val_loss: 2.3242 - val_acc: 0.4008\n",
            "Epoch 9/10\n",
            "7106/7106 [==============================] - 6s 813us/step - loss: 1.9871 - acc: 0.4341 - val_loss: 2.4177 - val_acc: 0.3986\n",
            "Epoch 10/10\n",
            "7106/7106 [==============================] - 6s 826us/step - loss: 1.9951 - acc: 0.4222 - val_loss: 2.3449 - val_acc: 0.4130\n",
            "Train on 7106 samples, validate on 2368 samples\n",
            "Epoch 1/10\n",
            "7106/7106 [==============================] - 6s 808us/step - loss: 2.0158 - acc: 0.4325 - val_loss: 2.1053 - val_acc: 0.4261\n",
            "Epoch 2/10\n",
            "7106/7106 [==============================] - 6s 808us/step - loss: 1.9804 - acc: 0.4357 - val_loss: 2.4139 - val_acc: 0.3965\n",
            "Epoch 3/10\n",
            "7106/7106 [==============================] - 6s 809us/step - loss: 1.9450 - acc: 0.4507 - val_loss: 2.1160 - val_acc: 0.4307\n",
            "Epoch 4/10\n",
            "7106/7106 [==============================] - 6s 809us/step - loss: 1.9138 - acc: 0.4560 - val_loss: 2.3517 - val_acc: 0.4063\n",
            "Epoch 5/10\n",
            "7106/7106 [==============================] - 6s 808us/step - loss: 1.8799 - acc: 0.4630 - val_loss: 2.0761 - val_acc: 0.4523\n",
            "Epoch 6/10\n",
            "7106/7106 [==============================] - 6s 808us/step - loss: 1.8768 - acc: 0.4630 - val_loss: 2.0630 - val_acc: 0.4481\n",
            "Epoch 7/10\n",
            "7106/7106 [==============================] - 6s 809us/step - loss: 1.8436 - acc: 0.4726 - val_loss: 2.0514 - val_acc: 0.4493\n",
            "Epoch 8/10\n",
            "7106/7106 [==============================] - 6s 810us/step - loss: 1.8275 - acc: 0.4765 - val_loss: 2.1766 - val_acc: 0.4329\n",
            "Epoch 9/10\n",
            "7106/7106 [==============================] - 6s 809us/step - loss: 1.7878 - acc: 0.4893 - val_loss: 2.2204 - val_acc: 0.4413\n",
            "Epoch 10/10\n",
            "7106/7106 [==============================] - 6s 823us/step - loss: 1.7605 - acc: 0.4986 - val_loss: 2.1294 - val_acc: 0.4362\n",
            "Train on 7106 samples, validate on 2368 samples\n",
            "Epoch 1/10\n",
            "7106/7106 [==============================] - 6s 808us/step - loss: 1.8187 - acc: 0.4795 - val_loss: 1.8016 - val_acc: 0.4937\n",
            "Epoch 2/10\n",
            "7106/7106 [==============================] - 6s 809us/step - loss: 1.7953 - acc: 0.4816 - val_loss: 1.6923 - val_acc: 0.5144\n",
            "Epoch 3/10\n",
            "7106/7106 [==============================] - 6s 808us/step - loss: 1.7513 - acc: 0.4958 - val_loss: 1.7153 - val_acc: 0.5131\n",
            "Epoch 4/10\n",
            "7106/7106 [==============================] - 6s 809us/step - loss: 1.7209 - acc: 0.5042 - val_loss: 1.7672 - val_acc: 0.5110\n",
            "Epoch 5/10\n",
            "7106/7106 [==============================] - 6s 808us/step - loss: 1.7064 - acc: 0.5094 - val_loss: 1.8076 - val_acc: 0.5068\n",
            "Epoch 6/10\n",
            "7106/7106 [==============================] - 6s 810us/step - loss: 1.6793 - acc: 0.5115 - val_loss: 1.8205 - val_acc: 0.4970\n",
            "Epoch 7/10\n",
            "7106/7106 [==============================] - 6s 809us/step - loss: 1.6593 - acc: 0.5173 - val_loss: 1.9509 - val_acc: 0.4793\n",
            "Epoch 8/10\n",
            "7106/7106 [==============================] - 6s 810us/step - loss: 1.6566 - acc: 0.5187 - val_loss: 1.8156 - val_acc: 0.4869\n",
            "Epoch 9/10\n",
            "7106/7106 [==============================] - 6s 809us/step - loss: 1.6078 - acc: 0.5383 - val_loss: 1.8968 - val_acc: 0.4886\n",
            "Epoch 10/10\n",
            "7106/7106 [==============================] - 6s 808us/step - loss: 1.5965 - acc: 0.5408 - val_loss: 1.8344 - val_acc: 0.4962\n",
            "0.3368876702278047 / 2.5291878854906233\n",
            "0.35209037242708974 / 2.4799486970579303\n",
            "0.37130489824591456 / 2.3966149011173763\n",
            "0.36475929074190755 / 2.4863803209485233\n",
            "0.3928420609114943 / 2.3083473532586485\n",
            "0.3893581081081081 / 2.321887665503734\n",
            "0.3945312483890636 / 2.3219690459805564\n",
            "0.395270271176422 / 2.3238935744440234\n",
            "0.4022381752729416 / 2.351365850583927\n",
            "0.4083614862851195 / 2.299803314982234\n",
            "Best val loss: 2.5291878854906233 & with acc: 2.5291878854906233 at epoch: 1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UcabAPVsZpEH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1652
        },
        "outputId": "f47a204a-6f91-4f0f-daa0-1ea0a345e597"
      },
      "source": [
        "\n",
        "\n",
        "def make_combined_CNN_RNN_model(input_shape):\n",
        "    nn = models.Sequential()\n",
        "    nn.add(layers.SeparableConv1D(64, 5, activation = 'relu',\n",
        "                                  input_shape = (None, input_shape[-1])))\n",
        "    nn.add(layers.BatchNormalization())\n",
        "    nn.add(layers.SeparableConv1D(64, 5, activation = 'relu'))\n",
        "    nn.add(layers.BatchNormalization())\n",
        "    nn.add(layers.MaxPooling1D(3))\n",
        "    nn.add(layers.Dropout(0.3))\n",
        "\n",
        "    nn.add(layers.SeparableConv1D(128, 5, activation = 'relu'))\n",
        "    nn.add(layers.BatchNormalization())\n",
        "    nn.add(layers.SeparableConv1D(128, 5, activation = 'relu'))\n",
        "    nn.add(layers.BatchNormalization())\n",
        "    # nn.add(layers.MaxPooling1D(3))\n",
        "    # nn.add(layers.Dropout(0.3))\n",
        "    #\n",
        "    # nn.add(layers.SeparableConv1D(512, 5, activation = 'relu'))\n",
        "    # nn.add(layers.BatchNormalization())\n",
        "    # nn.add(layers.SeparableConv1D(512, 5, activation = 'relu'))\n",
        "    # nn.add(layers.BatchNormalization())\n",
        "\n",
        "    nn.add(layers.Bidirectional(layers.LSTM(128, dropout = 0.3,\n",
        "                                            recurrent_dropout = 0.3,\n",
        "                                            return_sequences = True)))\n",
        "    nn.add(layers.Bidirectional(layers.LSTM(128, dropout = 0.3,\n",
        "                                            recurrent_dropout = 0.3,\n",
        "                                            return_sequences = True)))\n",
        "    nn.add(layers.Bidirectional(layers.LSTM(128, dropout = 0.3,\n",
        "                                            recurrent_dropout = 0.3,\n",
        "                                            return_sequences = True)))\n",
        "    nn.add(layers.Bidirectional(layers.LSTM(128, dropout = 0.3,\n",
        "                                            recurrent_dropout = 0.3)))\n",
        "\n",
        "    nn.add(layers.Dense(41, activation = 'softmax'))\n",
        "\n",
        "    return nn\n",
        "\n",
        "model = make_combined_CNN_RNN_model((max_timesteps, num_freq))\n",
        "model.compile(optimizer = 'rmsprop', loss = 'categorical_crossentropy',\n",
        "              metrics = ['accuracy'])\n",
        "\n",
        "k = 4\n",
        "num_val = len(train_data) // k\n",
        "num_train = len(train_labels) - num_val\n",
        "all_val_acc_histories, all_val_loss_histories = [], []\n",
        "for x in range(k):\n",
        "    val_data = train_data[x * num_val: (x + 1) * num_val]\n",
        "    val_labels = train_labels[x * num_val: (x + 1) * num_val]\n",
        "\n",
        "    partial_train_data = np.concatenate(\n",
        "        [train_data[: x * num_val], train_data[(x + 1) * num_val:]],\n",
        "        axis = 0)\n",
        "    partial_train_labels = np.concatenate(\n",
        "        [train_labels[: x * num_val],\n",
        "         train_labels[(x + 1) * num_val:]],\n",
        "        axis = 0)\n",
        "    \n",
        "    hst = model.fit(partial_train_data, partial_train_labels, batch_size = 1024,\n",
        "                    epochs = 10, validation_data = (val_data, val_labels),)\n",
        "\n",
        "    hst = hst.history\n",
        "    all_val_loss_histories.append(hst['val_loss'])\n",
        "    all_val_acc_histories.append(hst['val_acc'])\n",
        "\n",
        "avg_val_loss_hst = np.mean(all_val_loss_histories, axis = 0)\n",
        "avg_val_acc_hst = np.mean(all_val_acc_histories, axis = 0)\n",
        "\n",
        "best_loss, best_acc, prev_acc, best_epoch = None, None, None, 0\n",
        "\n",
        "acc_increased = True\n",
        "for i in range(10):\n",
        "    print(avg_val_acc_hst[i], '/', avg_val_loss_hst[i])\n",
        "\n",
        "    if prev_acc is not None and avg_val_acc_hst[i] < prev_acc:\n",
        "        acc_increased = False\n",
        "    prev_acc = avg_val_loss_hst[i]\n",
        "\n",
        "    if (best_acc is None or avg_val_acc_hst[i] > best_acc and\n",
        "            acc_increased):\n",
        "        best_acc = avg_val_acc_hst[i]\n",
        "        best_loss = avg_val_loss_hst[i]\n",
        "        best_epoch = i + 1\n",
        "\n",
        "print('Best val loss:', best_loss, '& with acc:', best_acc, 'at epoch:',\n",
        "      str(best_epoch))\n",
        "\n",
        "model.save('1DCNN_RNN.h5')"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 7106 samples, validate on 2368 samples\n",
            "Epoch 1/10\n",
            "7106/7106 [==============================] - 13s 2ms/step - loss: 3.5468 - acc: 0.0764 - val_loss: 3.3753 - val_acc: 0.1199\n",
            "Epoch 2/10\n",
            "7106/7106 [==============================] - 2s 217us/step - loss: 3.2932 - acc: 0.1203 - val_loss: 3.2199 - val_acc: 0.1410\n",
            "Epoch 3/10\n",
            "7106/7106 [==============================] - 2s 215us/step - loss: 3.1673 - acc: 0.1503 - val_loss: 3.1845 - val_acc: 0.1508\n",
            "Epoch 4/10\n",
            "7106/7106 [==============================] - 2s 214us/step - loss: 3.0714 - acc: 0.1689 - val_loss: 3.1538 - val_acc: 0.1715\n",
            "Epoch 5/10\n",
            "7106/7106 [==============================] - 2s 216us/step - loss: 2.9971 - acc: 0.1797 - val_loss: 3.1689 - val_acc: 0.1660\n",
            "Epoch 6/10\n",
            "7106/7106 [==============================] - 2s 218us/step - loss: 2.9317 - acc: 0.1929 - val_loss: 3.0140 - val_acc: 0.1934\n",
            "Epoch 7/10\n",
            "7106/7106 [==============================] - 2s 215us/step - loss: 2.9041 - acc: 0.1990 - val_loss: 2.9383 - val_acc: 0.2086\n",
            "Epoch 8/10\n",
            "7106/7106 [==============================] - 2s 213us/step - loss: 2.8419 - acc: 0.2198 - val_loss: 2.9370 - val_acc: 0.2010\n",
            "Epoch 9/10\n",
            "7106/7106 [==============================] - 2s 217us/step - loss: 2.7751 - acc: 0.2280 - val_loss: 2.9919 - val_acc: 0.2019\n",
            "Epoch 10/10\n",
            "7106/7106 [==============================] - 2s 214us/step - loss: 2.7558 - acc: 0.2301 - val_loss: 2.8746 - val_acc: 0.2200\n",
            "Train on 7106 samples, validate on 2368 samples\n",
            "Epoch 1/10\n",
            "7106/7106 [==============================] - 2s 215us/step - loss: 2.7542 - acc: 0.2298 - val_loss: 2.6175 - val_acc: 0.2720\n",
            "Epoch 2/10\n",
            "7106/7106 [==============================] - 2s 213us/step - loss: 2.7347 - acc: 0.2333 - val_loss: 2.5929 - val_acc: 0.2774\n",
            "Epoch 3/10\n",
            "7106/7106 [==============================] - 2s 216us/step - loss: 2.6788 - acc: 0.2534 - val_loss: 2.6084 - val_acc: 0.2665\n",
            "Epoch 4/10\n",
            "7106/7106 [==============================] - 2s 214us/step - loss: 2.6570 - acc: 0.2596 - val_loss: 2.6228 - val_acc: 0.2686\n",
            "Epoch 5/10\n",
            "7106/7106 [==============================] - 2s 213us/step - loss: 2.6252 - acc: 0.2612 - val_loss: 2.5492 - val_acc: 0.2931\n",
            "Epoch 6/10\n",
            "7106/7106 [==============================] - 2s 212us/step - loss: 2.5977 - acc: 0.2654 - val_loss: 2.5393 - val_acc: 0.2998\n",
            "Epoch 7/10\n",
            "7106/7106 [==============================] - 2s 213us/step - loss: 2.5512 - acc: 0.2771 - val_loss: 2.5528 - val_acc: 0.2876\n",
            "Epoch 8/10\n",
            "7106/7106 [==============================] - 2s 215us/step - loss: 2.5780 - acc: 0.2686 - val_loss: 2.5649 - val_acc: 0.2711\n",
            "Epoch 9/10\n",
            "7106/7106 [==============================] - 2s 212us/step - loss: 2.5257 - acc: 0.2900 - val_loss: 2.4735 - val_acc: 0.3184\n",
            "Epoch 10/10\n",
            "7106/7106 [==============================] - 2s 215us/step - loss: 2.5055 - acc: 0.2837 - val_loss: 2.5333 - val_acc: 0.3032\n",
            "Train on 7106 samples, validate on 2368 samples\n",
            "Epoch 1/10\n",
            "7106/7106 [==============================] - 2s 212us/step - loss: 2.5146 - acc: 0.2972 - val_loss: 2.3924 - val_acc: 0.3197\n",
            "Epoch 2/10\n",
            "7106/7106 [==============================] - 1s 211us/step - loss: 2.5185 - acc: 0.2907 - val_loss: 2.3768 - val_acc: 0.3121\n",
            "Epoch 3/10\n",
            "7106/7106 [==============================] - 2s 215us/step - loss: 2.4598 - acc: 0.3059 - val_loss: 2.3297 - val_acc: 0.3277\n",
            "Epoch 4/10\n",
            "7106/7106 [==============================] - 2s 211us/step - loss: 2.4526 - acc: 0.3042 - val_loss: 2.3732 - val_acc: 0.3264\n",
            "Epoch 5/10\n",
            "7106/7106 [==============================] - 2s 214us/step - loss: 2.4443 - acc: 0.3089 - val_loss: 2.3612 - val_acc: 0.3222\n",
            "Epoch 6/10\n",
            "7106/7106 [==============================] - 2s 213us/step - loss: 2.3993 - acc: 0.3180 - val_loss: 2.3748 - val_acc: 0.3256\n",
            "Epoch 7/10\n",
            "7106/7106 [==============================] - 2s 214us/step - loss: 2.3815 - acc: 0.3231 - val_loss: 2.4134 - val_acc: 0.3125\n",
            "Epoch 8/10\n",
            "7106/7106 [==============================] - 1s 210us/step - loss: 2.3608 - acc: 0.3299 - val_loss: 2.3427 - val_acc: 0.3315\n",
            "Epoch 9/10\n",
            "7106/7106 [==============================] - 2s 215us/step - loss: 2.3598 - acc: 0.3297 - val_loss: 2.3607 - val_acc: 0.3155\n",
            "Epoch 10/10\n",
            "7106/7106 [==============================] - 2s 213us/step - loss: 2.3617 - acc: 0.3341 - val_loss: 2.3384 - val_acc: 0.3277\n",
            "Train on 7106 samples, validate on 2368 samples\n",
            "Epoch 1/10\n",
            "7106/7106 [==============================] - 1s 211us/step - loss: 2.3779 - acc: 0.3245 - val_loss: 2.1724 - val_acc: 0.3725\n",
            "Epoch 2/10\n",
            "7106/7106 [==============================] - 2s 217us/step - loss: 2.3404 - acc: 0.3355 - val_loss: 2.1061 - val_acc: 0.3944\n",
            "Epoch 3/10\n",
            "7106/7106 [==============================] - 2s 214us/step - loss: 2.3426 - acc: 0.3299 - val_loss: 2.1578 - val_acc: 0.3704\n",
            "Epoch 4/10\n",
            "7106/7106 [==============================] - 2s 215us/step - loss: 2.3353 - acc: 0.3345 - val_loss: 2.1905 - val_acc: 0.3771\n",
            "Epoch 5/10\n",
            "7106/7106 [==============================] - 2s 215us/step - loss: 2.3078 - acc: 0.3458 - val_loss: 2.1869 - val_acc: 0.3733\n",
            "Epoch 6/10\n",
            "7106/7106 [==============================] - 2s 216us/step - loss: 2.2891 - acc: 0.3515 - val_loss: 2.1644 - val_acc: 0.3818\n",
            "Epoch 7/10\n",
            "7106/7106 [==============================] - 2s 218us/step - loss: 2.2547 - acc: 0.3545 - val_loss: 2.1739 - val_acc: 0.3784\n",
            "Epoch 8/10\n",
            "7106/7106 [==============================] - 2s 217us/step - loss: 2.2636 - acc: 0.3558 - val_loss: 2.2092 - val_acc: 0.3733\n",
            "Epoch 9/10\n",
            "7106/7106 [==============================] - 1s 211us/step - loss: 2.2443 - acc: 0.3574 - val_loss: 2.2010 - val_acc: 0.3704\n",
            "Epoch 10/10\n",
            "7106/7106 [==============================] - 2s 212us/step - loss: 2.2201 - acc: 0.3600 - val_loss: 2.1859 - val_acc: 0.3805\n",
            "0.27100929033917354 / 2.6393935309874044\n",
            "0.28124999959726593 / 2.573928994101447\n",
            "0.27882179013780645 / 2.5700892870490617\n",
            "0.285895271176422 / 2.5850954345754675\n",
            "0.28864020300475324 / 2.5665526228982047\n",
            "0.3001478050608893 / 2.523147151276872\n",
            "0.29676942507157456 / 2.5195683063687504\n",
            "0.29423564078437314 / 2.513448123996322\n",
            "0.3015202694648021 / 2.506787176067765\n",
            "0.3078547297297297 / 2.4830443166397713\n",
            "Best val loss: 2.6393935309874044 & with acc: 2.6393935309874044 at epoch: 1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2UODbmGDa-LH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1652
        },
        "outputId": "1f0681b4-add4-4b20-c3eb-b586fa18acce"
      },
      "source": [
        "from keras.applications import DenseNet121\n",
        "\n",
        "def make_dense_net_model(conv_base):\n",
        "    nn = models.Sequential()\n",
        "    nn.add(conv_base)\n",
        "    nn.add(layers.Dense(41, activation = 'softmax'))\n",
        "    return nn\n",
        "\n",
        "dn_base = DenseNet121(include_top = False,\n",
        "                      input_shape = (max_timesteps, num_freq, 3),\n",
        "                      pooling = 'avg')\n",
        "dn_base.trainable = True\n",
        "# print(dn_base.summary())\n",
        "# Fine-tuning\n",
        "set_trainable = False\n",
        "for layer in dn_base.layers:\n",
        "    if layer.name == 'conv5_block13_0_bn':\n",
        "        set_trainable = True\n",
        "    if set_trainable:\n",
        "        layer.trainable = True\n",
        "    else:\n",
        "        layer.trainable = False\n",
        "\n",
        "model = make_dense_net_model(dn_base)\n",
        "model.compile(optimizer = 'rmsprop', loss = 'categorical_crossentropy',\n",
        "              metrics = ['accuracy'])\n",
        "\n",
        "k = 4\n",
        "num_val = len(train_data) // k\n",
        "num_train = len(train_labels) - num_val\n",
        "all_val_acc_histories, all_val_loss_histories = [], []\n",
        "for x in range(k):\n",
        "    val_data = train_data[x * num_val: (x + 1) * num_val]\n",
        "    val_labels = train_labels[x * num_val: (x + 1) * num_val]\n",
        "\n",
        "    partial_train_data = np.concatenate(\n",
        "        [train_data[: x * num_val], train_data[(x + 1) * num_val:]],\n",
        "        axis = 0)\n",
        "    partial_train_labels = np.concatenate(\n",
        "        [train_labels[: x * num_val],\n",
        "         train_labels[(x + 1) * num_val:]],\n",
        "        axis = 0)\n",
        "    \n",
        "    partial_train_data = partial_train_data.reshape(\n",
        "            (partial_train_data.shape[0], \n",
        "             partial_train_data.shape[1], \n",
        "             partial_train_data.shape[2], \n",
        "             1))\n",
        "    \n",
        "    partial_train_data = np.repeat(partial_train_data, 3, axis=3)\n",
        "    \n",
        "    val_data = val_data.reshape(\n",
        "            (val_data.shape[0], \n",
        "             val_data.shape[1], \n",
        "             val_data.shape[2], \n",
        "             1))\n",
        "    \n",
        "    val_data = np.repeat(val_data, 3, axis=3)\n",
        "#     print('Val Data shape:')\n",
        "#     print(val_data.shape)\n",
        "\n",
        "    \n",
        "    hst = model.fit(partial_train_data, partial_train_labels, batch_size = 1024,\n",
        "                    epochs = 10, validation_data = (val_data, val_labels),)\n",
        "\n",
        "    hst = hst.history\n",
        "    all_val_loss_histories.append(hst['val_loss'])\n",
        "    all_val_acc_histories.append(hst['val_acc'])\n",
        "\n",
        "avg_val_loss_hst = np.mean(all_val_loss_histories, axis = 0)\n",
        "avg_val_acc_hst = np.mean(all_val_acc_histories, axis = 0)\n",
        "\n",
        "best_loss, best_acc, prev_acc, best_epoch = None, None, None, 0\n",
        "\n",
        "acc_increased = True\n",
        "for i in range(10):\n",
        "    print(avg_val_acc_hst[i], '/', avg_val_loss_hst[i])\n",
        "\n",
        "    if prev_acc is not None and avg_val_acc_hst[i] < prev_acc:\n",
        "        acc_increased = False\n",
        "    prev_acc = avg_val_loss_hst[i]\n",
        "\n",
        "    if (best_acc is None or avg_val_acc_hst[i] > best_acc and\n",
        "            acc_increased):\n",
        "        best_acc = avg_val_acc_hst[i]\n",
        "        best_loss = avg_val_loss_hst[i]\n",
        "        best_epoch = i + 1\n",
        "\n",
        "print('Best val loss:', best_loss, '& with acc:', best_acc, 'at epoch:',\n",
        "      str(best_epoch))\n",
        "\n",
        "model.save('DenseNetModel.h5')"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 7106 samples, validate on 2368 samples\n",
            "Epoch 1/10\n",
            "7106/7106 [==============================] - 46s 7ms/step - loss: 3.8703 - acc: 0.1057 - val_loss: 11.5388 - val_acc: 0.0498\n",
            "Epoch 2/10\n",
            "7106/7106 [==============================] - 2s 339us/step - loss: 2.7379 - acc: 0.2831 - val_loss: 11.3960 - val_acc: 0.0465\n",
            "Epoch 3/10\n",
            "7106/7106 [==============================] - 2s 337us/step - loss: 2.2438 - acc: 0.4021 - val_loss: 11.2043 - val_acc: 0.0460\n",
            "Epoch 4/10\n",
            "7106/7106 [==============================] - 2s 340us/step - loss: 1.8462 - acc: 0.5250 - val_loss: 10.9872 - val_acc: 0.0481\n",
            "Epoch 5/10\n",
            "7106/7106 [==============================] - 2s 341us/step - loss: 1.5243 - acc: 0.6288 - val_loss: 11.6239 - val_acc: 0.0460\n",
            "Epoch 6/10\n",
            "7106/7106 [==============================] - 2s 343us/step - loss: 1.2511 - acc: 0.7181 - val_loss: 11.2657 - val_acc: 0.0439\n",
            "Epoch 7/10\n",
            "7106/7106 [==============================] - 2s 340us/step - loss: 1.0402 - acc: 0.7812 - val_loss: 11.6753 - val_acc: 0.0477\n",
            "Epoch 8/10\n",
            "7106/7106 [==============================] - 2s 342us/step - loss: 0.8560 - acc: 0.8285 - val_loss: 11.9305 - val_acc: 0.0503\n",
            "Epoch 9/10\n",
            "7106/7106 [==============================] - 2s 341us/step - loss: 0.7230 - acc: 0.8560 - val_loss: 12.3689 - val_acc: 0.0384\n",
            "Epoch 10/10\n",
            "7106/7106 [==============================] - 2s 342us/step - loss: 0.6345 - acc: 0.8688 - val_loss: 11.5533 - val_acc: 0.0498\n",
            "Train on 7106 samples, validate on 2368 samples\n",
            "Epoch 1/10\n",
            "7106/7106 [==============================] - 2s 337us/step - loss: 1.4740 - acc: 0.6632 - val_loss: 11.5291 - val_acc: 0.0541\n",
            "Epoch 2/10\n",
            "7106/7106 [==============================] - 2s 336us/step - loss: 0.9598 - acc: 0.7677 - val_loss: 11.5288 - val_acc: 0.0519\n",
            "Epoch 3/10\n",
            "7106/7106 [==============================] - 2s 332us/step - loss: 0.7585 - acc: 0.8273 - val_loss: 11.3166 - val_acc: 0.0515\n",
            "Epoch 4/10\n",
            "7106/7106 [==============================] - 2s 331us/step - loss: 0.6298 - acc: 0.8611 - val_loss: 11.8900 - val_acc: 0.0439\n",
            "Epoch 5/10\n",
            "7106/7106 [==============================] - 2s 331us/step - loss: 0.5658 - acc: 0.8731 - val_loss: 11.6781 - val_acc: 0.0422\n",
            "Epoch 6/10\n",
            "7106/7106 [==============================] - 2s 329us/step - loss: 0.5276 - acc: 0.8743 - val_loss: 11.8687 - val_acc: 0.0443\n",
            "Epoch 7/10\n",
            "7106/7106 [==============================] - 2s 330us/step - loss: 0.4989 - acc: 0.8781 - val_loss: 11.9905 - val_acc: 0.0405\n",
            "Epoch 8/10\n",
            "7106/7106 [==============================] - 2s 328us/step - loss: 0.4923 - acc: 0.8759 - val_loss: 12.0197 - val_acc: 0.0490\n",
            "Epoch 9/10\n",
            "7106/7106 [==============================] - 2s 325us/step - loss: 0.4773 - acc: 0.8787 - val_loss: 12.1649 - val_acc: 0.0477\n",
            "Epoch 10/10\n",
            "7106/7106 [==============================] - 2s 329us/step - loss: 0.4621 - acc: 0.8774 - val_loss: 12.0810 - val_acc: 0.0427\n",
            "Train on 7106 samples, validate on 2368 samples\n",
            "Epoch 1/10\n",
            "7106/7106 [==============================] - 2s 328us/step - loss: 0.6874 - acc: 0.8172 - val_loss: 12.1218 - val_acc: 0.0507\n",
            "Epoch 2/10\n",
            "7106/7106 [==============================] - 2s 330us/step - loss: 0.5121 - acc: 0.8729 - val_loss: 12.1266 - val_acc: 0.0486\n",
            "Epoch 3/10\n",
            "7106/7106 [==============================] - 2s 330us/step - loss: 0.4793 - acc: 0.8736 - val_loss: 12.1462 - val_acc: 0.0524\n",
            "Epoch 4/10\n",
            "7106/7106 [==============================] - 2s 330us/step - loss: 0.4894 - acc: 0.8722 - val_loss: 12.7101 - val_acc: 0.0477\n",
            "Epoch 5/10\n",
            "7106/7106 [==============================] - 2s 332us/step - loss: 0.4632 - acc: 0.8739 - val_loss: 12.7153 - val_acc: 0.0545\n",
            "Epoch 6/10\n",
            "7106/7106 [==============================] - 2s 330us/step - loss: 0.4561 - acc: 0.8766 - val_loss: 12.7385 - val_acc: 0.0460\n",
            "Epoch 7/10\n",
            "7106/7106 [==============================] - 2s 332us/step - loss: 0.4616 - acc: 0.8757 - val_loss: 13.0438 - val_acc: 0.0477\n",
            "Epoch 8/10\n",
            "7106/7106 [==============================] - 2s 331us/step - loss: 0.4550 - acc: 0.8745 - val_loss: 12.9050 - val_acc: 0.0486\n",
            "Epoch 9/10\n",
            "7106/7106 [==============================] - 2s 331us/step - loss: 0.4554 - acc: 0.8748 - val_loss: 13.3777 - val_acc: 0.0443\n",
            "Epoch 10/10\n",
            "7106/7106 [==============================] - 2s 331us/step - loss: 0.4519 - acc: 0.8756 - val_loss: 13.3320 - val_acc: 0.0473\n",
            "Train on 7106 samples, validate on 2368 samples\n",
            "Epoch 1/10\n",
            "7106/7106 [==============================] - 2s 324us/step - loss: 0.5147 - acc: 0.8631 - val_loss: 13.4786 - val_acc: 0.0486\n",
            "Epoch 2/10\n",
            "7106/7106 [==============================] - 2s 324us/step - loss: 0.4525 - acc: 0.8797 - val_loss: 13.5230 - val_acc: 0.0498\n",
            "Epoch 3/10\n",
            "7106/7106 [==============================] - 2s 326us/step - loss: 0.4377 - acc: 0.8802 - val_loss: 13.5819 - val_acc: 0.0431\n",
            "Epoch 4/10\n",
            "7106/7106 [==============================] - 2s 329us/step - loss: 0.4331 - acc: 0.8784 - val_loss: 13.5856 - val_acc: 0.0456\n",
            "Epoch 5/10\n",
            "7106/7106 [==============================] - 2s 327us/step - loss: 0.4329 - acc: 0.8804 - val_loss: 13.4532 - val_acc: 0.0553\n",
            "Epoch 6/10\n",
            "7106/7106 [==============================] - 2s 328us/step - loss: 0.4371 - acc: 0.8790 - val_loss: 14.0748 - val_acc: 0.0389\n",
            "Epoch 7/10\n",
            "7106/7106 [==============================] - 2s 327us/step - loss: 0.4291 - acc: 0.8780 - val_loss: 13.7813 - val_acc: 0.0511\n",
            "Epoch 8/10\n",
            "7106/7106 [==============================] - 2s 327us/step - loss: 0.4345 - acc: 0.8790 - val_loss: 13.9858 - val_acc: 0.0490\n",
            "Epoch 9/10\n",
            "7106/7106 [==============================] - 2s 327us/step - loss: 0.4300 - acc: 0.8794 - val_loss: 13.8031 - val_acc: 0.0566\n",
            "Epoch 10/10\n",
            "7106/7106 [==============================] - 2s 328us/step - loss: 0.4271 - acc: 0.8804 - val_loss: 13.8539 - val_acc: 0.0562\n",
            "0.05078124992448736 / 12.167063094474175\n",
            "0.04919763503445161 / 12.143606772293914\n",
            "0.04824746634207062 / 12.062243667808739\n",
            "0.04634712837837837 / 12.29324264784117\n",
            "0.04951435813327898 / 12.36764415534767\n",
            "0.04328547302331474 / 12.486927580189061\n",
            "0.04676942570084656 / 12.622717960460767\n",
            "0.04919763503445161 / 12.71025249764726\n",
            "0.046769425549821275 / 12.92864585567165\n",
            "0.048986486536828244 / 12.705041994919648\n",
            "Best val loss: 12.167063094474175 & with acc: 0.05078124992448736 at epoch: 1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RimUBVXglSku",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 463
        },
        "outputId": "b2a32e35-a836-488f-dc99-b2a54626ed9a"
      },
      "source": [
        "\n",
        "\n",
        "def make_1DCNN_model(input_shape):\n",
        "    nn = models.Sequential()\n",
        "    nn.add(layers.SeparableConv1D(64, 7, activation = 'relu',\n",
        "                                  input_shape = (None, input_shape[-1])))\n",
        "    nn.add(layers.BatchNormalization())\n",
        "    nn.add(layers.SeparableConv1D(64, 7, activation = 'relu'))\n",
        "    nn.add(layers.BatchNormalization())\n",
        "    nn.add(layers.MaxPooling1D(5))\n",
        "    nn.add(layers.Dropout(0.3))\n",
        "\n",
        "    nn.add(layers.SeparableConv1D(128, 7, activation = 'relu'))\n",
        "    nn.add(layers.BatchNormalization())\n",
        "    nn.add(layers.SeparableConv1D(128, 7, activation = 'relu'))\n",
        "    nn.add(layers.BatchNormalization())\n",
        "    nn.add(layers.MaxPooling1D(5))\n",
        "    nn.add(layers.Dropout(0.3))\n",
        "\n",
        "    nn.add(layers.SeparableConv1D(512, 7, activation = 'relu'))\n",
        "    nn.add(layers.BatchNormalization())\n",
        "    nn.add(layers.SeparableConv1D(512, 7, activation = 'relu'))\n",
        "    nn.add(layers.BatchNormalization())\n",
        "    nn.add(layers.GlobalAveragePooling1D())\n",
        "\n",
        "    nn.add(layers.Dense(41, activation = 'softmax'))\n",
        "\n",
        "    return nn\n",
        "\n",
        "model = make_1DCNN_model((max_timesteps, num_freq))\n",
        "model.compile(optimizer = 'rmsprop', loss = 'categorical_crossentropy',\n",
        "              metrics = ['accuracy'])\n",
        "\n",
        "k = 4\n",
        "num_val = len(train_data) // k\n",
        "num_train = len(train_labels) - num_val\n",
        "all_val_acc_histories, all_val_loss_histories = [], []\n",
        "for x in range(k):\n",
        "    val_data = train_data[x * num_val: (x + 1) * num_val]\n",
        "    val_labels = train_labels[x * num_val: (x + 1) * num_val]\n",
        "\n",
        "    partial_train_data = np.concatenate(\n",
        "        [train_data[: x * num_val], train_data[(x + 1) * num_val:]],\n",
        "        axis = 0)\n",
        "    partial_train_labels = np.concatenate(\n",
        "        [train_labels[: x * num_val],\n",
        "         train_labels[(x + 1) * num_val:]],\n",
        "        axis = 0)\n",
        "    \n",
        "    hst = model.fit(partial_train_data, partial_train_labels, batch_size = 1024,\n",
        "                    epochs = 10, validation_data = (val_data, val_labels),)\n",
        "\n",
        "    hst = hst.history\n",
        "    all_val_loss_histories.append(hst['val_loss'])\n",
        "    all_val_acc_histories.append(hst['val_acc'])\n",
        "\n",
        "avg_val_loss_hst = np.mean(all_val_loss_histories, axis = 0)\n",
        "avg_val_acc_hst = np.mean(all_val_acc_histories, axis = 0)\n",
        "\n",
        "best_loss, best_acc, prev_acc, best_epoch = None, None, None, 0\n",
        "\n",
        "acc_increased = True\n",
        "for i in range(10):\n",
        "    print(avg_val_acc_hst[i], '/', avg_val_loss_hst[i])\n",
        "\n",
        "    if prev_acc is not None and avg_val_acc_hst[i] < prev_acc:\n",
        "        acc_increased = False\n",
        "    prev_acc = avg_val_loss_hst[i]\n",
        "\n",
        "    if (best_acc is None or avg_val_acc_hst[i] > best_acc and\n",
        "            acc_increased):\n",
        "        best_acc = avg_val_acc_hst[i]\n",
        "        best_loss = avg_val_loss_hst[i]\n",
        "        best_epoch = i + 1\n",
        "\n",
        "print('Best val loss:', best_loss, '& with acc:', best_acc, 'at epoch:',\n",
        "      str(best_epoch))\n",
        "\n",
        "model.save('1DCNN.h5')\n"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 7106 samples, validate on 2368 samples\n",
            "Epoch 1/10\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "InvalidArgumentError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mInvalidArgumentError\u001b[0m                      Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-29-c8721997813f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     49\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     50\u001b[0m     hst = model.fit(partial_train_data, partial_train_labels, batch_size = 1024,\n\u001b[0;32m---> 51\u001b[0;31m                     epochs = 10, validation_data = (val_data, val_labels),)\n\u001b[0m\u001b[1;32m     52\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     53\u001b[0m     \u001b[0mhst\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhst\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhistory\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, **kwargs)\u001b[0m\n\u001b[1;32m   1037\u001b[0m                                         \u001b[0minitial_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minitial_epoch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1038\u001b[0m                                         \u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1039\u001b[0;31m                                         validation_steps=validation_steps)\n\u001b[0m\u001b[1;32m   1040\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1041\u001b[0m     def evaluate(self, x=None, y=None,\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/keras/engine/training_arrays.py\u001b[0m in \u001b[0;36mfit_loop\u001b[0;34m(model, f, ins, out_labels, batch_size, epochs, verbose, callbacks, val_f, val_ins, shuffle, callback_metrics, initial_epoch, steps_per_epoch, validation_steps)\u001b[0m\n\u001b[1;32m    197\u001b[0m                     \u001b[0mins_batch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mins_batch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtoarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    198\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 199\u001b[0;31m                 \u001b[0mouts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mins_batch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    200\u001b[0m                 \u001b[0mouts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mto_list\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mouts\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    201\u001b[0m                 \u001b[0;32mfor\u001b[0m \u001b[0ml\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mo\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout_labels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mouts\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m   2713\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_legacy_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2714\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2715\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2716\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2717\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mpy_any\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mis_tensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m   2673\u001b[0m             \u001b[0mfetched\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_callable_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0marray_vals\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_metadata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2674\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2675\u001b[0;31m             \u001b[0mfetched\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_callable_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0marray_vals\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2676\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mfetched\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2677\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1437\u001b[0m           ret = tf_session.TF_SessionRunCallable(\n\u001b[1;32m   1438\u001b[0m               \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_handle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstatus\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1439\u001b[0;31m               run_metadata_ptr)\n\u001b[0m\u001b[1;32m   1440\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1441\u001b[0m           \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/errors_impl.py\u001b[0m in \u001b[0;36m__exit__\u001b[0;34m(self, type_arg, value_arg, traceback_arg)\u001b[0m\n\u001b[1;32m    526\u001b[0m             \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    527\u001b[0m             \u001b[0mcompat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mc_api\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_Message\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstatus\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstatus\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 528\u001b[0;31m             c_api.TF_GetCode(self.status.status))\n\u001b[0m\u001b[1;32m    529\u001b[0m     \u001b[0;31m# Delete the underlying status object from memory otherwise it stays alive\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    530\u001b[0m     \u001b[0;31m# as there is a reference to status from this from the traceback due to\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mInvalidArgumentError\u001b[0m: Computed output size would be negative: -2 [input_size: 4, effective_filter_size: 7, stride: 1]\n\t [[{{node separable_conv1d_8/separable_conv2d/depthwise}}]]\n\t [[{{node metrics_10/acc/Mean}}]]"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tCOWN6mzmJBE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def make_RNN_model(input_shape):\n",
        "    nn = models.Sequential()\n",
        "    nn.add(layers.Bidirectional(layers.LSTM(64, dropout = 0.3,\n",
        "                                            recurrent_dropout = 0.3,\n",
        "                                            return_sequences = True,\n",
        "                                            input_shape =\n",
        "                                            (None, input_shape[-1]))))\n",
        "    nn.add(layers.Bidirectional(layers.LSTM(64, dropout = 0.3,\n",
        "                                            recurrent_dropout = 0.3,\n",
        "                                            return_sequences = True)))\n",
        "    nn.add(layers.Bidirectional(layers.LSTM(64, dropout = 0.3,\n",
        "                                            recurrent_dropout = 0.3,\n",
        "                                            return_sequences = True)))\n",
        "    nn.add(layers.Bidirectional(layers.LSTM(64, dropout = 0.3,\n",
        "                                            recurrent_dropout = 0.3)))\n",
        "    nn.add(layers.Dense(41, activation = 'softmax'))\n",
        "\n",
        "    return nn\n",
        "\n",
        "\n",
        "model = make_RNN_model((max_timesteps, num_freq))\n",
        "model.compile(optimizer = 'rmsprop', loss = 'categorical_crossentropy',\n",
        "              metrics = ['accuracy'])\n",
        "\n",
        "k = 4\n",
        "num_val = len(train_data) // k\n",
        "num_train = len(train_labels) - num_val\n",
        "all_val_acc_histories, all_val_loss_histories = [], []\n",
        "for x in range(k):\n",
        "    val_data = train_data[x * num_val: (x + 1) * num_val]\n",
        "    val_labels = train_labels[x * num_val: (x + 1) * num_val]\n",
        "\n",
        "    partial_train_data = np.concatenate(\n",
        "        [train_data[: x * num_val], train_data[(x + 1) * num_val:]],\n",
        "        axis = 0)\n",
        "    partial_train_labels = np.concatenate(\n",
        "        [train_labels[: x * num_val],\n",
        "         train_labels[(x + 1) * num_val:]],\n",
        "        axis = 0)\n",
        "    \n",
        "    hst = model.fit(partial_train_data, partial_train_labels, batch_size = 1024,\n",
        "                    epochs = 10, validation_data = (val_data, val_labels),)\n",
        "\n",
        "    hst = hst.history\n",
        "    all_val_loss_histories.append(hst['val_loss'])\n",
        "    all_val_acc_histories.append(hst['val_acc'])\n",
        "\n",
        "avg_val_loss_hst = np.mean(all_val_loss_histories, axis = 0)\n",
        "avg_val_acc_hst = np.mean(all_val_acc_histories, axis = 0)\n",
        "\n",
        "best_loss, best_acc, prev_acc, best_epoch = None, None, None, 0\n",
        "\n",
        "acc_increased = True\n",
        "for i in range(10):\n",
        "    print(avg_val_acc_hst[i], '/', avg_val_loss_hst[i])\n",
        "\n",
        "    if prev_acc is not None and avg_val_acc_hst[i] < prev_acc:\n",
        "        acc_increased = False\n",
        "    prev_acc = avg_val_loss_hst[i]\n",
        "\n",
        "    if (best_acc is None or avg_val_acc_hst[i] > best_acc and\n",
        "            acc_increased):\n",
        "        best_acc = avg_val_acc_hst[i]\n",
        "        best_loss = avg_val_loss_hst[i]\n",
        "        best_epoch = i + 1\n",
        "\n",
        "print('Best val loss:', best_loss, '& with acc:', best_acc, 'at epoch:',\n",
        "      str(best_epoch))\n",
        "\n",
        "model.save('RNN.h5')\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w6T_IeSRm0ha",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Reorganize all data as sets of flat vectors of 0-1.0 values\n",
        "# Shape (..., 28, 28, 1)\n",
        "\n",
        "test_data_2D = test_data.reshape(\n",
        "               (test_data.shape[0], \n",
        "                test_data.shape[1], \n",
        "                test_data.shape[2], \n",
        "                1))\n",
        "    \n",
        "test_data_2D_3chan = np.repeat(test_data_2D, 3, axis=3)\n",
        "    \n",
        "\n",
        "cnn2D = models.load_model('2DCNN.h5')\n",
        "rnn = models.load_model('RNN.h5')\n",
        "cnn1D = models.load_model('1DCNN.h5')\n",
        "cnn_rnn = models.load_model('1DCNN_RNN.h5')\n",
        "dense_net = models.load_model('DenseNetModel.h5')\n",
        "\n",
        "cnn2D_preds = cnn2D.predict(test_data_2D)\n",
        "rnn_preds = rnn.predict(test_data)\n",
        "cnn1D_preds = cnn1D.predict(test_data)\n",
        "cnn_rnn_preds = cnn_rnn.predict(test_data)\n",
        "dense_net_preds = dense_net.predict(test_data_2D_3chan)\n",
        "\n",
        "final_preds = 0.2 * (cnn2D_preds + rnn_preds + cnn1D_preds + \n",
        "                     cnn_rnn_preds + dense_net_preds)\n",
        "\n",
        "with open('TestTags.txt', 'w') as ttf:\n",
        "    for i, res in enumerate(final_preds):\n",
        "        ttf.write(i+')', unique_labels[res.index(1.)])\n",
        "\n",
        "\n",
        "# Prompt for user to enter a wav filename then predicts??\n",
        "\n",
        "# batch_size = 128\n",
        "# results = nn.predict(test_data, batch_size = batch_size)\n",
        "# for result in results:\n",
        "#     res = np.argmax(result)\n",
        "#     print(res)"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}