classification_cnn_vgg16.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 3046,
     "status": "ok",
     "timestamp": 1554052938056,
     "user": {
      "displayName": "Mood Class",
      "photoUrl": "",
      "userId": "05879208426753522283"
     },
     "user_tz": -180
    },
    "id": "yxfW96s3QfaY",
    "outputId": "d819dcee-92e5-4d3c-b19c-7508b4a80ca9"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\", category = FutureWarning)\n",
    "\n",
    "import os\n",
    "import keras\n",
    "import h5py\n",
    "import librosa\n",
    "import itertools\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from collections import OrderedDict\n",
    "\n",
    "from keras.utils import to_categorical\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.layers import Activation\n",
    "from keras.layers import Flatten\n",
    "from keras.layers import Input\n",
    "from keras.layers import Dropout\n",
    "from keras.models import Model\n",
    "from keras.applications.vgg16 import VGG16\n",
    "from tensorflow.keras.callbacks import EarlyStopping"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "LofJlezKQfai"
   },
   "source": [
    "# Read the data\n",
    "\n",
    "> Helper functions to assist the process to read songs, split then and return an array of spectrograms/melspectrograms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "i2IeHtfrQfaj"
   },
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "@description: Method to split a song into multiple songs using overlapping windows\n",
    "\"\"\"\n",
    "def splitsongs(X, y, window = 0.1, overlap = 0.5):\n",
    "    # Empty lists to hold our results\n",
    "    temp_X = []\n",
    "    temp_y = []\n",
    "\n",
    "    # Get the input song array size\n",
    "    xshape = X.shape[0]\n",
    "    chunk = int(xshape*window)\n",
    "    offset = int(chunk*(1.-overlap))\n",
    "    \n",
    "    # Split the song and create new ones on windows\n",
    "    spsong = [X[i:i+chunk] for i in range(0, xshape - chunk + offset, offset)]\n",
    "    for s in spsong:\n",
    "        temp_X.append(s)\n",
    "        temp_y.append(y)\n",
    "\n",
    "    return np.array(temp_X), np.array(temp_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "dKxqynk7Qfan"
   },
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "@description: Method to convert a list of songs to a np array of melspectrograms\n",
    "\"\"\"\n",
    "def to_melspectrogram(songs, n_fft = 1024, hop_length = 512):\n",
    "    # Transformation function\n",
    "    melspec = lambda x: librosa.feature.melspectrogram(x, n_fft = n_fft,\n",
    "        hop_length = hop_length)[:,:,np.newaxis]\n",
    "\n",
    "    # map transformation of input songs to melspectrogram using log-scale\n",
    "    tsongs = map(melspec, songs)\n",
    "    return np.array(list(tsongs))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "FqWlFiPSQfaq"
   },
   "outputs": [],
   "source": [
    "def read_data(src_dir, genres, song_samples, spec_format, debug = True):    \n",
    "    # Empty array of dicts with the processed features from all files\n",
    "    arr_specs = []\n",
    "    arr_genres = []\n",
    "\n",
    "    # Read files from the folders\n",
    "    for x,_ in genres.items():\n",
    "        folder = src_dir + x\n",
    "        \n",
    "        for root, subdirs, files in os.walk(folder):\n",
    "            for file in files:\n",
    "                # Read the audio file\n",
    "                file_name = folder + \"/\" + file\n",
    "                signal, sr = librosa.load(file_name)\n",
    "                if len(signal) < song_samples:\n",
    "                    print('Error in {}\\nNum of samples ( >= {}): {}'.format(file_name, song_samples, len(signal)))\n",
    "                signal = signal[:song_samples]\n",
    "                \n",
    "                # Debug process\n",
    "                if debug:\n",
    "                    print(\"Reading file: {}\".format(file_name))\n",
    "                \n",
    "                # Convert to dataset of spectograms/melspectograms\n",
    "                signals, y = splitsongs(signal, genres[x])\n",
    "                \n",
    "                # Convert to \"spec\" representation\n",
    "                specs = spec_format(signals)\n",
    "                \n",
    "                # Save files\n",
    "                arr_genres.extend(y)\n",
    "                arr_specs.extend(specs)\n",
    "                \n",
    "                \n",
    "    return np.array(arr_specs), np.array(arr_genres)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "vjKaRSYRQfau"
   },
   "outputs": [],
   "source": [
    "# Parameters\n",
    "gtzan_dir = './gtzan/'\n",
    "song_samples = 660000\n",
    "genres = {'metal': 0, 'disco': 1, 'classical': 2, 'hiphop': 3, 'jazz': 4, \n",
    "          'country': 5, 'pop': 6, 'blues': 7, 'reggae': 8, 'rock': 9}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 139
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 94770,
     "status": "ok",
     "timestamp": 1554053029819,
     "user": {
      "displayName": "Mood Class",
      "photoUrl": "",
      "userId": "05879208426753522283"
     },
     "user_tz": -180
    },
    "id": "2AmziFJZQfay",
    "outputId": "eaa7bbe8-611a-4b58-accb-3bee00864675"
   },
   "outputs": [],
   "source": [
    "# Read the data\n",
    "# X, y = read_data(gtzan_dir, genres, song_samples, to_melspectrogram, debug=True)\n",
    "# np.save('./data/x_gtzan_npy.npy', X)\n",
    "# np.save('./data/y_gtzan_npy.npy', y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 153702,
     "status": "ok",
     "timestamp": 1554053088791,
     "user": {
      "displayName": "Mood Class",
      "photoUrl": "",
      "userId": "05879208426753522283"
     },
     "user_tz": -180
    },
    "id": "qWpT21rXQfa5",
    "outputId": "627deeec-13e4-4ac7-b87e-c7977220d992"
   },
   "outputs": [],
   "source": [
    "X = np.load('./data/x_gtzan_npy.npy')\n",
    "y = np.load('./data/y_gtzan_npy.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "u08a2XxAQfa8"
   },
   "outputs": [],
   "source": [
    "# One hot encoding of the labels\n",
    "y = to_categorical(y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "RZTR4QAjQfbA"
   },
   "source": [
    "# Dataset Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 157118,
     "status": "ok",
     "timestamp": 1554053092225,
     "user": {
      "displayName": "Mood Class",
      "photoUrl": "",
      "userId": "05879208426753522283"
     },
     "user_tz": -180
    },
    "id": "s2s3p-EMQfbB",
    "outputId": "5311fde0-ddf2-4d80-ba0f-5bdd894f764b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(19000, 128, 129, 3)"
      ]
     },
     "execution_count": 9,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_stack = np.squeeze(np.stack((X,) * 3, -1))\n",
    "X_stack.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "3KGYdj6oQfbG"
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X_stack, y, test_size=0.3, random_state=42, stratify = y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 159994,
     "status": "ok",
     "timestamp": 1554053095125,
     "user": {
      "displayName": "Mood Class",
      "photoUrl": "",
      "userId": "05879208426753522283"
     },
     "user_tz": -180
    },
    "id": "olPVl0BqQfbJ",
    "outputId": "02c656e6-606f-4fff-a0a4-c2e02e17a75b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(13300, 128, 129, 3) (5700, 128, 129, 3) (13300, 10) (5700, 10)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape, X_test.shape, y_train.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 347
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 160773,
     "status": "ok",
     "timestamp": 1554053095978,
     "user": {
      "displayName": "Mood Class",
      "photoUrl": "",
      "userId": "05879208426753522283"
     },
     "user_tz": -180
    },
    "id": "tSXPiqdSQfbO",
    "outputId": "b8f870d8-722e-42aa-f617-be95ee2b11f5"
   },
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Histogram for train and test \n",
    "values, count = np.unique(np.argmax(y_train, axis=1), return_counts=True)\n",
    "plt.bar(values, count)\n",
    "\n",
    "values, count = np.unique(np.argmax(y_test, axis=1), return_counts=True)\n",
    "plt.bar(values, count)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ZptzK0b3QfbU"
   },
   "source": [
    "# Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 989
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 164350,
     "status": "ok",
     "timestamp": 1554053099590,
     "user": {
      "displayName": "Mood Class",
      "photoUrl": "",
      "userId": "05879208426753522283"
     },
     "user_tz": -180
    },
    "id": "R9gtL1xhQfbV",
    "outputId": "4040db2f-e311-42d0-e241-aa6133866e37"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Colocations handled automatically by placer.\n",
      "Downloading data from https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
      "58892288/58889256 [==============================] - 1s 0us/step\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n",
      "Creating EarlyStopping Callback ...\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         (None, 128, 129, 3)       0         \n",
      "_________________________________________________________________\n",
      "block1_conv1 (Conv2D)        (None, 128, 129, 64)      1792      \n",
      "_________________________________________________________________\n",
      "block1_conv2 (Conv2D)        (None, 128, 129, 64)      36928     \n",
      "_________________________________________________________________\n",
      "block1_pool (MaxPooling2D)   (None, 64, 64, 64)        0         \n",
      "_________________________________________________________________\n",
      "block2_conv1 (Conv2D)        (None, 64, 64, 128)       73856     \n",
      "_________________________________________________________________\n",
      "block2_conv2 (Conv2D)        (None, 64, 64, 128)       147584    \n",
      "_________________________________________________________________\n",
      "block2_pool (MaxPooling2D)   (None, 32, 32, 128)       0         \n",
      "_________________________________________________________________\n",
      "block3_conv1 (Conv2D)        (None, 32, 32, 256)       295168    \n",
      "_________________________________________________________________\n",
      "block3_conv2 (Conv2D)        (None, 32, 32, 256)       590080    \n",
      "_________________________________________________________________\n",
      "block3_conv3 (Conv2D)        (None, 32, 32, 256)       590080    \n",
      "_________________________________________________________________\n",
      "block3_pool (MaxPooling2D)   (None, 16, 16, 256)       0         \n",
      "_________________________________________________________________\n",
      "block4_conv1 (Conv2D)        (None, 16, 16, 512)       1180160   \n",
      "_________________________________________________________________\n",
      "block4_conv2 (Conv2D)        (None, 16, 16, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block4_conv3 (Conv2D)        (None, 16, 16, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block4_pool (MaxPooling2D)   (None, 8, 8, 512)         0         \n",
      "_________________________________________________________________\n",
      "block5_conv1 (Conv2D)        (None, 8, 8, 512)         2359808   \n",
      "_________________________________________________________________\n",
      "block5_conv2 (Conv2D)        (None, 8, 8, 512)         2359808   \n",
      "_________________________________________________________________\n",
      "block5_conv3 (Conv2D)        (None, 8, 8, 512)         2359808   \n",
      "_________________________________________________________________\n",
      "block5_pool (MaxPooling2D)   (None, 4, 4, 512)         0         \n",
      "_________________________________________________________________\n",
      "sequential_1 (Sequential)    (None, 10)                2099978   \n",
      "=================================================================\n",
      "Total params: 16,814,666\n",
      "Trainable params: 16,702,090\n",
      "Non-trainable params: 112,576\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Model Definition\n",
    "input_shape = X_train[0].shape\n",
    "num_genres = 10\n",
    "\n",
    "def cnn_vgg16(input_shape, num_genres, freezed_layers):\n",
    "    input_tensor = Input(shape=input_shape)\n",
    "    vgg16 = VGG16(include_top=False, weights='imagenet',\n",
    "                  input_tensor=input_tensor)\n",
    "\n",
    "    top = Sequential()\n",
    "    top.add(Flatten(input_shape=vgg16.output_shape[1:]))\n",
    "    top.add(Dense(256, activation='relu'))\n",
    "    top.add(Dropout(0.5))\n",
    "    top.add(Dense(num_genres, activation='softmax'))\n",
    "\n",
    "    model = Model(inputs=vgg16.input, outputs=top(vgg16.output))\n",
    "    for layer in model.layers[:freezed_layers]:\n",
    "        layer.trainable = False\n",
    "\n",
    "    return model\n",
    "\n",
    "model = cnn_vgg16(input_shape, num_genres, 5)\n",
    "print(\"Creating EarlyStopping Callback ...\")\n",
    "early_stopping_callback = EarlyStopping(monitor='val_acc', patience=5)\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1346
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 174266,
     "status": "ok",
     "timestamp": 1554056477332,
     "user": {
      "displayName": "Mood Class",
      "photoUrl": "",
      "userId": "05879208426753522283"
     },
     "user_tz": -180
    },
    "id": "tnNrv73gQfbZ",
    "outputId": "575842a3-2a67-4ae7-f2ca-00302c11f891"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.cast instead.\n",
      "Train on 13300 samples, validate on 5700 samples\n",
      "Epoch 1/1000\n",
      "13300/13300 [==============================] - 112s 8ms/step - loss: 2.1468 - acc: 0.2668 - val_loss: 1.6959 - val_acc: 0.3786\n",
      "Epoch 2/1000\n",
      "13300/13300 [==============================] - 93s 7ms/step - loss: 1.6221 - acc: 0.4169 - val_loss: 1.4218 - val_acc: 0.4895\n",
      "Epoch 3/1000\n",
      "13300/13300 [==============================] - 93s 7ms/step - loss: 1.3679 - acc: 0.5139 - val_loss: 1.1692 - val_acc: 0.5867\n",
      "Epoch 4/1000\n",
      "13300/13300 [==============================] - 93s 7ms/step - loss: 1.2791 - acc: 0.5429 - val_loss: 1.1145 - val_acc: 0.5981\n",
      "Epoch 5/1000\n",
      "13300/13300 [==============================] - 93s 7ms/step - loss: 1.1251 - acc: 0.6019 - val_loss: 0.9850 - val_acc: 0.6532\n",
      "Epoch 6/1000\n",
      "13300/13300 [==============================] - 93s 7ms/step - loss: 0.9795 - acc: 0.6550 - val_loss: 0.9344 - val_acc: 0.6691\n",
      "Epoch 7/1000\n",
      "13300/13300 [==============================] - 93s 7ms/step - loss: 0.8938 - acc: 0.6857 - val_loss: 0.8784 - val_acc: 0.6965\n",
      "Epoch 8/1000\n",
      "13300/13300 [==============================] - 93s 7ms/step - loss: 0.8277 - acc: 0.7089 - val_loss: 0.7463 - val_acc: 0.7309\n",
      "Epoch 9/1000\n",
      "13300/13300 [==============================] - 93s 7ms/step - loss: 0.7529 - acc: 0.7351 - val_loss: 0.7800 - val_acc: 0.7268\n",
      "Epoch 10/1000\n",
      "13300/13300 [==============================] - 94s 7ms/step - loss: 0.6506 - acc: 0.7736 - val_loss: 0.6655 - val_acc: 0.7733\n",
      "Epoch 11/1000\n",
      "13300/13300 [==============================] - 94s 7ms/step - loss: 0.5732 - acc: 0.8004 - val_loss: 0.6639 - val_acc: 0.7744\n",
      "Epoch 12/1000\n",
      "13300/13300 [==============================] - 94s 7ms/step - loss: 0.5218 - acc: 0.8192 - val_loss: 0.7090 - val_acc: 0.7654\n",
      "Epoch 13/1000\n",
      "13300/13300 [==============================] - 93s 7ms/step - loss: 0.5186 - acc: 0.8244 - val_loss: 0.5741 - val_acc: 0.8067\n",
      "Epoch 14/1000\n",
      "13300/13300 [==============================] - 93s 7ms/step - loss: 0.5070 - acc: 0.8343 - val_loss: 0.5673 - val_acc: 0.8102\n",
      "Epoch 15/1000\n",
      "13300/13300 [==============================] - 93s 7ms/step - loss: 0.3876 - acc: 0.8707 - val_loss: 0.4615 - val_acc: 0.8421\n",
      "Epoch 16/1000\n",
      "13300/13300 [==============================] - 93s 7ms/step - loss: 0.4136 - acc: 0.8655 - val_loss: 0.4683 - val_acc: 0.8389\n",
      "Epoch 17/1000\n",
      "13300/13300 [==============================] - 93s 7ms/step - loss: 0.3826 - acc: 0.8740 - val_loss: 0.5698 - val_acc: 0.8119\n",
      "Epoch 18/1000\n",
      "13300/13300 [==============================] - 93s 7ms/step - loss: 0.3377 - acc: 0.8883 - val_loss: 0.5018 - val_acc: 0.8411\n",
      "Epoch 19/1000\n",
      "13300/13300 [==============================] - 93s 7ms/step - loss: 0.2615 - acc: 0.9131 - val_loss: 0.4639 - val_acc: 0.8498\n",
      "Epoch 20/1000\n",
      "13300/13300 [==============================] - 93s 7ms/step - loss: 0.2956 - acc: 0.9026 - val_loss: 0.5426 - val_acc: 0.8198\n",
      "Epoch 21/1000\n",
      "13300/13300 [==============================] - 93s 7ms/step - loss: 0.2751 - acc: 0.9098 - val_loss: 0.4189 - val_acc: 0.8684\n",
      "Epoch 22/1000\n",
      "13300/13300 [==============================] - 93s 7ms/step - loss: 0.2418 - acc: 0.9208 - val_loss: 0.4940 - val_acc: 0.8512\n",
      "Epoch 23/1000\n",
      "13300/13300 [==============================] - 93s 7ms/step - loss: 0.2377 - acc: 0.9220 - val_loss: 0.4654 - val_acc: 0.8554\n",
      "Epoch 24/1000\n",
      "13300/13300 [==============================] - 93s 7ms/step - loss: 0.2095 - acc: 0.9307 - val_loss: 0.4708 - val_acc: 0.8558\n",
      "Epoch 25/1000\n",
      "13300/13300 [==============================] - 93s 7ms/step - loss: 0.1640 - acc: 0.9460 - val_loss: 0.5812 - val_acc: 0.8430\n",
      "Epoch 26/1000\n",
      "13300/13300 [==============================] - 93s 7ms/step - loss: 0.2184 - acc: 0.9279 - val_loss: 0.3953 - val_acc: 0.8795\n",
      "Epoch 27/1000\n",
      "13300/13300 [==============================] - 93s 7ms/step - loss: 0.1782 - acc: 0.9412 - val_loss: 0.5762 - val_acc: 0.8468\n",
      "Epoch 28/1000\n",
      "13300/13300 [==============================] - 93s 7ms/step - loss: 0.2433 - acc: 0.9236 - val_loss: 0.4977 - val_acc: 0.8656\n",
      "Epoch 29/1000\n",
      "13300/13300 [==============================] - 93s 7ms/step - loss: 0.1913 - acc: 0.9387 - val_loss: 0.4201 - val_acc: 0.8772\n",
      "Epoch 30/1000\n",
      "13300/13300 [==============================] - 93s 7ms/step - loss: 0.1521 - acc: 0.9483 - val_loss: 0.4461 - val_acc: 0.8812\n",
      "Epoch 31/1000\n",
      "13300/13300 [==============================] - 93s 7ms/step - loss: 0.1325 - acc: 0.9563 - val_loss: 0.3339 - val_acc: 0.9025\n",
      "Epoch 32/1000\n",
      "13300/13300 [==============================] - 93s 7ms/step - loss: 0.1263 - acc: 0.9586 - val_loss: 0.4318 - val_acc: 0.8895\n",
      "Epoch 33/1000\n",
      "13300/13300 [==============================] - 93s 7ms/step - loss: 0.1366 - acc: 0.9561 - val_loss: 0.4268 - val_acc: 0.8821\n",
      "Epoch 34/1000\n",
      "13300/13300 [==============================] - 93s 7ms/step - loss: 0.1331 - acc: 0.9579 - val_loss: 0.5352 - val_acc: 0.8705\n",
      "Epoch 35/1000\n",
      "13300/13300 [==============================] - 93s 7ms/step - loss: 0.1290 - acc: 0.9594 - val_loss: 0.5528 - val_acc: 0.8625\n",
      "Epoch 36/1000\n",
      "13300/13300 [==============================] - 93s 7ms/step - loss: 0.1283 - acc: 0.9596 - val_loss: 0.3936 - val_acc: 0.8921\n",
      "Learning stoped on epoch: 35\n"
     ]
    }
   ],
   "source": [
    "model.compile(loss=keras.losses.categorical_crossentropy,\n",
    "              optimizer=keras.optimizers.Adam(),\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "hist = model.fit(X_train, y_train,\n",
    "          batch_size=128,\n",
    "          epochs=1000,\n",
    "          verbose=1,\n",
    "          validation_data=(X_test, y_test),\n",
    "          shuffle=True,\n",
    "          callbacks=[early_stopping_callback])\n",
    "print(\"Learning stoped on epoch:\", early_stopping_callback.stopped_epoch)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "zvU9I4hoW9WY"
   },
   "source": [
    "#Save the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1670,
     "status": "ok",
     "timestamp": 1554056478999,
     "user": {
      "displayName": "Mood Class",
      "photoUrl": "",
      "userId": "05879208426753522283"
     },
     "user_tz": -180
    },
    "id": "JHh8UKqUWzNh",
    "outputId": "1567b61b-215c-47d9-8de5-dc31b2c06736"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model saved to disk!\n"
     ]
    }
   ],
   "source": [
    "# Save the model\n",
    "model.save('./weights/genres_full_vgg16.h5')\n",
    "print(\"Model saved to disk!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 18691,
     "status": "ok",
     "timestamp": 1554056497686,
     "user": {
      "displayName": "Mood Class",
      "photoUrl": "",
      "userId": "05879208426753522283"
     },
     "user_tz": -180
    },
    "id": "X7omzUJ3Qfbf",
    "outputId": "bedad51a-a469-44b5-ea14-f9b89b031510"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val_loss = 0.394 and val_acc = 0.892\n"
     ]
    }
   ],
   "source": [
    "score = model.evaluate(X_test, y_test, verbose=0)\n",
    "print(\"val_loss = {:.3f} and val_acc = {:.3f}\".format(score[0], score[1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "yCtKGzPoXFX6"
   },
   "source": [
    "#Visualisation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 509
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1275,
     "status": "ok",
     "timestamp": 1554056498957,
     "user": {
      "displayName": "Mood Class",
      "photoUrl": "",
      "userId": "05879208426753522283"
     },
     "user_tz": -180
    },
    "id": "SYk8WD-gQfbm",
    "outputId": "ff162d95-e556-4589-9c27-112eafee0bd6"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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TBgBWrVrO5s0bWyxOEZG2qqS8hrW70vifN3bz36/uZOWOVCqq6xgVH8YTCwbyfz8dyQ/G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\n",
      "text/plain": [
       "<Figure size 1080x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,7))\n",
    "\n",
    "plt.subplot(1,2,1)\n",
    "plt.plot(hist.history['acc'], label='train')\n",
    "plt.plot(hist.history['val_acc'], label='validation')\n",
    "plt.title('Accuracy')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend()\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "plt.plot(hist.history['loss'], label='train')\n",
    "plt.plot(hist.history['val_loss'], label='validation')\n",
    "plt.title('Loss')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "gSK_Vw8OQfbv"
   },
   "outputs": [],
   "source": [
    "#http://scikit-learn.org/stable/auto_examples/model_selection/plot_confusion_matrix.html\n",
    "def plot_confusion_matrix(cm, classes,\n",
    "                          normalize=False,\n",
    "                          title='Confusion matrix',\n",
    "                          cmap=plt.cm.Blues):\n",
    "    \"\"\"\n",
    "    This function prints and plots the confusion matrix.\n",
    "    Normalization can be applied by setting `normalize=True`.\n",
    "    \"\"\"\n",
    "    if normalize:\n",
    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "        print(\"Normalized confusion matrix\")\n",
    "    else:\n",
    "        print('Confusion matrix, without normalization')\n",
    "\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title)\n",
    "    plt.colorbar()\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    plt.xticks(tick_marks, classes, rotation=45)\n",
    "    plt.yticks(tick_marks, classes)\n",
    "\n",
    "    fmt = '.2f' if normalize else 'd'\n",
    "    thresh = cm.max() / 2.\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        plt.text(j, i, format(cm[i, j], fmt),\n",
    "                 horizontalalignment=\"center\",\n",
    "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "c_sqcp22Qfb0"
   },
   "outputs": [],
   "source": [
    "preds = np.argmax(model.predict(X_test), axis = 1)\n",
    "y_orig = np.argmax(y_test, axis = 1)\n",
    "cm = confusion_matrix(preds, y_orig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 598
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1574,
     "status": "ok",
     "timestamp": 1554056516833,
     "user": {
      "displayName": "Mood Class",
      "photoUrl": "",
      "userId": "05879208426753522283"
     },
     "user_tz": -180
    },
    "id": "B8j41rYuQfb2",
    "outputId": "75d3ef82-cea8-4fe1-9db7-f72be02d2b68"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normalized confusion matrix\n"
     ]
    },
    {
     "data": {
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HubJAY39rK819xWDobSixkSAvL1/z2Mqy+fLzy74GiUScDE9j8G0okSBfuw21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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "keys = OrderedDict(sorted(genres.items(), key=lambda t: t[1])).keys()\n",
    "\n",
    "plt.figure(figsize=(8,8))\n",
    "plot_confusion_matrix(cm, keys, normalize=True)"
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [],
   "name": "classification_cnn_vgg16.ipynb",
   "provenance": [],
   "version": "0.3.2"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}