Pipeline.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pipeline from start to end for Image2Midi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Import all necessary modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:\n",
      "The TensorFlow contrib module will not be included in TensorFlow 2.0.\n",
      "For more information, please see:\n",
      "  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n",
      "  * https://github.com/tensorflow/addons\n",
      "  * https://github.com/tensorflow/io (for I/O related ops)\n",
      "If you depend on functionality not listed there, please file an issue.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from collections import namedtuple\n",
    "import copy as ccopy\n",
    "import os\n",
    "import sys\n",
    "\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "\n",
    "from magenta.models.music_vae import TrainedModel\n",
    "from magenta.models.music_vae import configs\n",
    "from magenta import music\n",
    "\n",
    "import joblib\n",
    "from PIL import Image\n",
    "\n",
    "np.random.seed(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Load tf-lite model as usual keras models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "class WrapperTFLite:\n",
    "    '''Class implementing a wrapper for tf-lite models so it can be used with \n",
    "    usual keras.Model's methods.\n",
    "\n",
    "    Attributes:\n",
    "        interpreter (tensorflow.lite.Interpreter obj): interpreter for the model.\n",
    "        input_details (dict): properties about an input image, including it's shape and dtype.\n",
    "        output_details (dict): properties about an output, including it's shape and dtype.\n",
    "        \n",
    "    '''\n",
    "    __slots__ = ['interpreter', 'input_details', 'output_details']\n",
    "    def __init__(self, path):\n",
    "        '''Constructor of tf-lite wrapper class. In constructor you have to define a path\n",
    "        to the tf lite model\n",
    "\n",
    "            Args:\n",
    "                path (str): path to a tensorflow lite model.\n",
    "\n",
    "        '''\n",
    "        self.interpreter = tf.lite.Interpreter(path)\n",
    "        self.interpreter.allocate_tensors()\n",
    "        # obtaining the input-output shapes and types\n",
    "        self.input_details = self.interpreter.get_input_details()\n",
    "        self.output_details = self.interpreter.get_output_details()\n",
    "\n",
    "    def predict(self, data):\n",
    "        '''Convinient method to use trained model over all data \n",
    "        (tensorflow lite models can predict only one image per time).\n",
    "\n",
    "            Args:\n",
    "                data (numpy.ndarray): array as input for the model.\n",
    "\n",
    "            Returns:\n",
    "                predictions (numpy.ndarray): model's output array.\n",
    "\n",
    "        '''\n",
    "        prediction = []\n",
    "        # use tqdm for progress bar\n",
    "        for i in range(data.shape[0]):\n",
    "            # resize input image so it corresponds to the input shape\n",
    "            img = np.array(Image.fromarray(data[i]).resize(self.input_details[0]['shape'][1:-1], Image.BICUBIC))\n",
    "            self.interpreter.set_tensor(self.input_details[0]['index'], img.reshape(1, *img.shape)[:,:,:,::-1])\n",
    "            # running inference\n",
    "            self.interpreter.invoke()\n",
    "            # get result and append to prediction list\n",
    "            prediction.extend(self.interpreter.get_tensor(self.output_details[0]['index']))\n",
    "        return np.array(prediction)\n",
    "\n",
    "# load trained tf lite model\n",
    "network_tflite = WrapperTFLite('colors_mobilenet.tflite')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Load musicVAE model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From d:\\captcha\\synvae\\lib\\site-packages\\magenta\\models\\music_vae\\trained_model.py:58: The name tf.gfile.IsDirectory is deprecated. Please use tf.io.gfile.isdir instead.\n",
      "\n",
      "WARNING:tensorflow:From d:\\captcha\\synvae\\lib\\site-packages\\magenta\\models\\music_vae\\base_model.py:157: The name tf.logging.info is deprecated. Please use tf.compat.v1.logging.info instead.\n",
      "\n",
      "INFO:tensorflow:Building MusicVAE model with BidirectionalLstmEncoder, CategoricalLstmDecoder, and hparams:\n",
      "{'max_seq_len': 32, 'z_size': 512, 'free_bits': 0, 'max_beta': 0.5, 'beta_rate': 0.99999, 'batch_size': 3072, 'grad_clip': 1.0, 'clip_mode': 'global_norm', 'grad_norm_clip_to_zero': 10000, 'learning_rate': 0.001, 'decay_rate': 0.9999, 'min_learning_rate': 1e-05, 'conditional': True, 'dec_rnn_size': [2048, 2048, 2048], 'enc_rnn_size': [2048], 'dropout_keep_prob': 1.0, 'sampling_schedule': 'inverse_sigmoid', 'sampling_rate': 1000, 'use_cudnn': False, 'residual_encoder': False, 'residual_decoder': False}\n",
      "WARNING:tensorflow:From d:\\captcha\\synvae\\lib\\site-packages\\magenta\\models\\music_vae\\base_model.py:161: The name tf.train.get_or_create_global_step is deprecated. Please use tf.compat.v1.train.get_or_create_global_step instead.\n",
      "\n",
      "INFO:tensorflow:\n",
      "Encoder Cells (bidirectional):\n",
      "  units: [2048]\n",
      "\n",
      "WARNING:tensorflow:From d:\\captcha\\synvae\\lib\\site-packages\\magenta\\models\\music_vae\\lstm_models.py:103: The name tf.VariableScope is deprecated. Please use tf.compat.v1.VariableScope instead.\n",
      "\n",
      "WARNING:tensorflow:From d:\\captcha\\synvae\\lib\\site-packages\\magenta\\models\\music_vae\\lstm_utils.py:45: MultiRNNCell.__init__ (from tensorflow.python.ops.rnn_cell_impl) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "This class is equivalent as tf.keras.layers.StackedRNNCells, and will be replaced by that in Tensorflow 2.0.\n",
      "INFO:tensorflow:\n",
      "Decoder Cells:\n",
      "  units: [2048, 2048, 2048]\n",
      "\n",
      "WARNING:tensorflow:From d:\\captcha\\synvae\\lib\\site-packages\\magenta\\models\\music_vae\\lstm_utils.py:200: The name tf.logging.warning is deprecated. Please use tf.compat.v1.logging.warning instead.\n",
      "\n",
      "WARNING:tensorflow:Setting non-training sampling schedule from inverse_sigmoid:1000.000000 to constant:1.0.\n",
      "WARNING:tensorflow:From d:\\captcha\\synvae\\lib\\site-packages\\magenta\\models\\music_vae\\lstm_utils.py:208: The name tf.train.get_global_step is deprecated. Please use tf.compat.v1.train.get_global_step instead.\n",
      "\n",
      "WARNING:tensorflow:From d:\\captcha\\synvae\\lib\\site-packages\\magenta\\models\\music_vae\\lstm_utils.py:208: to_float (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",
      "WARNING:tensorflow:From d:\\captcha\\synvae\\lib\\site-packages\\magenta\\models\\music_vae\\lstm_utils.py:231: The name tf.summary.scalar is deprecated. Please use tf.compat.v1.summary.scalar instead.\n",
      "\n",
      "WARNING:tensorflow:From d:\\captcha\\synvae\\lib\\site-packages\\magenta\\models\\music_vae\\trained_model.py:72: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
      "\n",
      "WARNING:tensorflow:From d:\\captcha\\synvae\\lib\\site-packages\\magenta\\models\\music_vae\\lstm_utils.py:162: dense (from tensorflow.python.layers.core) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use keras.layers.Dense instead.\n",
      "WARNING:tensorflow:From d:\\captcha\\synvae\\lib\\site-packages\\tensorflow_core\\python\\layers\\core.py:187: Layer.apply (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use `layer.__call__` method instead.\n",
      "WARNING:tensorflow:From d:\\captcha\\synvae\\lib\\site-packages\\tensorflow_core\\contrib\\rnn\\python\\ops\\lstm_ops.py:360: Layer.add_variable (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use `layer.add_weight` method instead.\n",
      "WARNING:tensorflow:From d:\\captcha\\synvae\\lib\\site-packages\\tensorflow_core\\contrib\\seq2seq\\python\\ops\\decoder.py:420: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
      "WARNING:tensorflow:From d:\\captcha\\synvae\\lib\\site-packages\\tensorflow_core\\contrib\\rnn\\python\\ops\\rnn.py:239: bidirectional_dynamic_rnn (from tensorflow.python.ops.rnn) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use `keras.layers.Bidirectional(keras.layers.RNN(cell))`, which is equivalent to this API\n",
      "WARNING:tensorflow:From d:\\captcha\\synvae\\lib\\site-packages\\tensorflow_core\\python\\ops\\rnn.py:464: dynamic_rnn (from tensorflow.python.ops.rnn) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use `keras.layers.RNN(cell)`, which is equivalent to this API\n",
      "WARNING:tensorflow:From d:\\captcha\\synvae\\lib\\site-packages\\magenta\\models\\music_vae\\trained_model.py:122: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.\n",
      "\n",
      "WARNING:tensorflow:From d:\\captcha\\synvae\\lib\\site-packages\\magenta\\models\\music_vae\\trained_model.py:123: The name tf.train.Saver is deprecated. Please use tf.compat.v1.train.Saver instead.\n",
      "\n",
      "INFO:tensorflow:Restoring parameters from ../models/cat-mel_2bar_big.ckpt\n"
     ]
    }
   ],
   "source": [
    "Args = namedtuple('Args', ' '.join(['batch_size', 'beta', 'data_path', 'epochs', 'exp_path',\n",
    "                                    'export_step', 'init_epoch', 'init_vis_epoch',\n",
    "                                    'musicvae_config', 'musicvae_path', 'task']))\n",
    "args = Args(1024 * 3, 1.0, '', 200, '../exp/mnist_syn_small', 5, 101, 0,\n",
    "            'cat-mel_2bar_big', '../models/cat-mel_2bar_big.ckpt', 'mnist')\n",
    "\n",
    "sys.path.append(\"./\")\n",
    "##########     Initialization   ########\n",
    "with tf.device('/gpu:0'):\n",
    "    config = tf.compat.v1.ConfigProto(allow_soft_placement=True)\n",
    "    with tf.compat.v1.Session(config=config) as sess:\n",
    "        net = TrainedModel(\n",
    "            config=ccopy.deepcopy(ccopy.deepcopy(configs.CONFIG_MAP[args.musicvae_config])),\n",
    "            batch_size=args.batch_size,\n",
    "            checkpoint_dir_or_path=args.musicvae_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Define embeddings center clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "black [ 0.13708812 -0.25628036 -0.03549532  0.31956847  0.25054769 -0.43554231\n",
      " -0.07915982 -0.72031518 -0.04400819]\n",
      "blue [ 0.41636727 -0.44927439  0.1734663   0.1293266  -0.50094518  0.02114657\n",
      " -0.03176783 -0.44402799  0.28354683]\n",
      "green [-0.2437847  -0.44592469  0.22885615  0.70248853 -0.12507642  0.28127119\n",
      "  0.19020899  0.04150721  0.2015764 ]\n",
      "grey [ 0.48354217 -0.25749983  0.20004847  0.51602418 -0.04620761 -0.29798859\n",
      "  0.09925249 -0.47640641  0.10601573]\n",
      "orange [ 0.04593691  0.21202949  0.06637516  0.34766234  0.30468157  0.06773892\n",
      " -0.01104144 -0.81365534 -0.08368759]\n",
      "purple [ 0.08178884  0.29706324 -0.0124259   0.5189675  -0.52587114 -0.30481781\n",
      " -0.12331348 -0.32207717 -0.32446807]\n",
      "red [ 0.07709255  0.17995854 -0.14509952  0.55190561  0.20090736  0.36339223\n",
      " -0.1045307  -0.50895308 -0.40295803]\n",
      "white [ 0.6121843  -0.03808442  0.2172002   0.58969564 -0.04374496 -0.14786779\n",
      "  0.22761592 -0.26653425  0.09595956]\n",
      "yellow [ 0.19398918  0.18506303  0.43629375  0.54948712 -0.09516009 -0.13309834\n",
      "  0.20862747 -0.29085519  0.36142056]\n"
     ]
    }
   ],
   "source": [
    "clusters = [cluster.mean(axis=0) for cluster in joblib.load(\"embed.gz\")]\n",
    "color = [\"black\", \"blue\", \"green\", \"grey\", \"orange\", \"purple\", \"red\", \"white\", \"yellow\"]\n",
    "for i, cluster in enumerate(clusters):\n",
    "    print(color[i], cluster)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. Load transition matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(200, 200)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def tanh(x):\n",
    "    '''Apply tanh nonlinearity to input array.\n",
    "\n",
    "            Args:\n",
    "                x (numpy.ndarray): array.\n",
    "\n",
    "            Returns:\n",
    "                t (numpy.ndarray): array after applying tanh activation.\n",
    "\n",
    "    '''\n",
    "    exp = np.exp(x)\n",
    "    exp_ = np.exp(-x)\n",
    "    t = (exp - exp_)/(exp + exp_)\n",
    "    return t\n",
    "\n",
    "random_gaussians = joblib.load(\"gaussians200.dat\")\n",
    "weights_of_transition = joblib.load(\"best_weights_pen5.gz\")\n",
    "len(random_gaussians), len(weights_of_transition)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6. Image2Vec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=256x256 at 0x50D8E10>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image = np.uint8(Image.open(\"test0.jpg\") \\\n",
    "                      .resize(network_tflite.input_details[0]['shape'][1:-1], \n",
    "                              Image.BICUBIC))[:, :, ::-1]\n",
    "Image.fromarray(image[:, :, ::-1]).resize((256, 256))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.25807926  0.16583322 -0.0618044   0.57158756  0.2679046   0.24816616\n",
      "  -0.06058797 -0.6059745  -0.2666964 ]]\n",
      "Nearest cluster is red\n"
     ]
    }
   ],
   "source": [
    "# ENCODE to color vector\n",
    "encoding = network_tflite.predict(image.reshape(*network_tflite.input_details[0]['shape']))\n",
    "print(encoding)\n",
    "nearest = ((np.array(clusters) - encoding)**2).sum(axis=1).argsort()\n",
    "print(\"Nearest cluster is\", color[nearest[0]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7. Vec2Midi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-9.41881956e-02, -1.05878283e+00, -7.10218550e-01,  3.46013822e-01,\n",
       "       -6.41934506e-01, -1.08949526e+00, -3.15459592e-01, -9.21174321e-01,\n",
       "       -8.22500921e-01, -1.36856484e-01,  4.93277662e-01, -6.82075562e-04,\n",
       "        4.59903361e-01, -4.41326827e-02,  4.38261881e-01,  1.91537480e-01,\n",
       "       -4.82805304e-02,  9.06368690e-01,  5.01567599e-01, -4.91562020e-01,\n",
       "        6.55908309e-01,  3.33510044e-01, -4.21062532e-01, -8.40263618e-02,\n",
       "        2.75978168e-01,  7.09730041e-01, -2.14073175e-01, -5.29448959e-01,\n",
       "        8.88396192e-01, -2.37490026e-01,  2.38117731e-01, -3.06329217e-01,\n",
       "       -3.35168761e-01,  4.45661484e-01, -4.72758271e-01,  1.39651082e-01,\n",
       "       -5.24575093e-01,  1.60835500e-02,  4.07977698e-01, -7.55419421e-02,\n",
       "        7.82988635e-01,  8.15268541e-01,  1.10728708e-01,  5.58343649e-01,\n",
       "       -3.75943377e-01, -9.65638777e-01, -3.81696294e-01,  3.63479264e-02,\n",
       "        4.14351552e-01, -1.20648090e-01, -5.11318374e-01, -1.66846138e-01,\n",
       "       -5.09765645e-01, -1.50733475e-01,  8.37794048e-01,  5.82057146e-01,\n",
       "       -2.58477562e-01,  3.24655330e-02, -3.92403547e-01, -1.28627211e-01,\n",
       "        5.36135463e-03,  2.08425795e-01,  3.54642300e-01,  3.83321315e-01,\n",
       "       -5.24055531e-01,  2.58885565e-01, -3.72961365e-01, -7.20291542e-01,\n",
       "       -6.95040553e-02, -4.88625578e-01, -7.16270532e-01, -9.11439538e-01,\n",
       "        3.67254511e-01,  3.01165190e-01,  2.37608424e-01, -3.98387004e-01,\n",
       "       -4.14536926e-01,  8.72786129e-02, -7.65850130e-01,  2.80979871e-01,\n",
       "       -7.95176269e-01,  5.57585348e-01, -5.63169160e-01,  1.65904823e-02,\n",
       "        7.30481078e-04, -7.25183780e-01,  2.26140415e-01, -2.55166458e-01,\n",
       "       -1.25969990e-01, -9.68435810e-01, -3.87911397e-01, -5.27862908e-01,\n",
       "       -1.19988292e-01,  5.36491499e-01, -3.97663557e-01, -1.45024775e-01,\n",
       "       -6.82513811e-03,  5.16442201e-01, -2.19290152e-02, -1.13157474e-01,\n",
       "       -8.31521223e-01,  9.70654369e-01,  6.35763240e-01, -7.69859132e-01,\n",
       "        3.68651818e-01,  7.62012730e-01,  5.67325935e-01, -3.37223962e-01,\n",
       "        3.04475714e-01,  2.04594604e-01,  4.26656311e-01,  6.02990881e-01,\n",
       "       -1.24142937e-01, -2.51194003e-01,  2.12133205e-02, -6.98360855e-01,\n",
       "        5.88069405e-01, -2.87000095e-01,  4.50837641e-01,  4.70257163e-01,\n",
       "        3.33888343e-01,  9.64827980e-02,  6.60793709e-01,  5.91191964e-02,\n",
       "       -1.03582126e-01,  4.24660982e-02, -2.81603653e-01,  7.19971791e-02,\n",
       "       -4.42100213e-01, -4.96831184e-01,  6.33254034e-01, -2.01579691e-01,\n",
       "       -1.54424432e-01,  3.35880746e-01,  8.40926475e-01,  2.38487864e-01,\n",
       "       -9.37678782e-01, -3.66060384e-01, -5.08451769e-01,  6.33965689e-01,\n",
       "       -3.46188156e-01,  4.11243699e-01, -1.10704583e+00,  3.05297063e-02,\n",
       "        1.44107625e-01,  1.18386488e+00, -6.48283748e-01, -4.64643224e-01,\n",
       "        3.53099458e-01, -2.23234501e-01, -3.64382210e-01,  2.55707230e-01,\n",
       "        4.07103736e-01,  1.19060239e+00,  2.47175021e-01, -3.82884811e-01,\n",
       "        4.92067003e-01, -2.46130503e-01, -3.02956927e-01,  6.56186002e-03,\n",
       "       -1.13902132e-01,  4.46155091e-02, -2.36637025e-01, -1.11222640e+00,\n",
       "        9.81908046e-01,  8.71412554e-01,  7.42888417e-01,  8.00960396e-02,\n",
       "       -8.34205436e-01,  4.11703022e-01,  2.31465221e-01, -9.94532851e-01,\n",
       "        1.01863327e+00, -7.16647832e-01,  3.31860410e-01, -9.31520569e-03,\n",
       "        1.36881758e-01,  2.41238164e-01,  7.71844321e-01, -1.56779401e-01,\n",
       "       -5.16588025e-02, -6.31138569e-01, -8.89639948e-01,  3.07242673e-01,\n",
       "        7.12674734e-01,  3.61865920e-02, -6.68662076e-01, -2.64305634e-01,\n",
       "        4.48308309e-01,  2.57935129e-01, -8.42518981e-01,  4.39417885e-01,\n",
       "        2.73540741e-01,  1.61987532e-01, -2.76845054e-01,  1.23426653e-01,\n",
       "        4.10299438e-01, -5.64659342e-01,  6.47505209e-01, -5.59206323e-03,\n",
       "        3.61284076e-01, -2.98018680e-01,  4.10073218e-01, -3.90458971e-01,\n",
       "        9.48384351e-01, -2.29312962e-02, -2.41049648e-01, -2.26480326e-01,\n",
       "        4.90050717e-01, -9.12020864e-01,  7.93198786e-01,  1.18564196e+00,\n",
       "       -3.75457222e-01,  7.78609108e-02, -3.74198315e-01, -3.59411203e-01,\n",
       "        3.62229746e-01, -1.83334061e-01,  3.99722620e-01, -1.37006766e-01,\n",
       "        1.46014767e-01, -2.74497833e-02, -7.79342288e-01,  7.82201454e-01,\n",
       "        6.35487460e-01,  2.22044155e-02,  3.76256195e-01,  2.58426578e-01,\n",
       "        7.47328737e-01, -2.02121080e-01, -3.05577174e-01,  6.06374358e-01,\n",
       "        2.89908519e-01, -1.41863052e+00,  1.10346731e-01,  3.56203091e-02,\n",
       "       -2.56594090e-01,  3.11148111e-01,  6.53174198e-01,  2.41479410e-01,\n",
       "        5.47336548e-01,  4.60581814e-01,  2.91697141e-01, -5.44972420e-02,\n",
       "       -5.01711707e-01,  3.86221007e-02,  2.72952551e-03, -4.99915511e-01,\n",
       "       -5.75595483e-02, -5.64813619e-01, -7.10249092e-01, -3.54042229e-01,\n",
       "       -8.68655453e-02,  2.73148201e-01,  8.91108713e-01,  5.37425732e-01,\n",
       "        2.30425427e-01,  2.66239336e-01, -5.51665706e-01, -1.17172726e-01,\n",
       "       -2.92783870e-01, -8.41170284e-01,  1.79920783e-01, -5.11232547e-01,\n",
       "       -2.85469934e-01,  2.85597118e-01, -7.94280263e-01, -6.27750178e-01,\n",
       "       -2.92913409e-01,  3.99498432e-02, -9.48204001e-01, -7.17534739e-01,\n",
       "        1.50366112e-01,  4.12200072e-01, -3.09769543e-01,  3.13752208e-01,\n",
       "       -5.06196775e-01, -3.37558761e-01, -1.08972803e+00, -1.07137565e-01,\n",
       "       -1.34561859e+00,  3.82544962e-01, -3.11939916e-01, -2.96875150e-01,\n",
       "        7.44951041e-01, -3.67702158e-01,  3.26806963e-01, -6.36384667e-01,\n",
       "        3.76260932e-01,  1.17982727e-01, -1.30540941e-01, -3.10135777e-01,\n",
       "       -3.46095739e-02, -3.04974739e-01, -6.15397808e-02,  5.68513994e-01,\n",
       "       -8.19937259e-01, -1.39530635e-01,  2.93743741e-02, -3.81830657e-01,\n",
       "        1.03514804e-01, -7.25133065e-01,  7.92547577e-01,  2.44725334e-01,\n",
       "        1.37368059e-01, -1.83034702e-01, -2.19332694e-02,  3.56625145e-01,\n",
       "       -2.03989795e-01, -5.66314726e-02,  8.03449396e-01, -3.30819864e-03,\n",
       "        4.30550521e-01,  9.08874898e-02, -1.35236815e-01, -1.14828665e-01,\n",
       "        4.37304877e-02,  5.05464804e-01,  4.92575638e-02, -5.85084471e-01,\n",
       "        4.57650134e-01, -3.60402429e-01, -8.39792921e-01, -4.97186194e-01,\n",
       "       -5.06631179e-01,  6.46131985e-01,  7.62956800e-01,  4.38903395e-01,\n",
       "        2.43645209e-01, -3.92423995e-01, -3.49743517e-02, -1.98618591e-01,\n",
       "        4.68252589e-01, -4.45618537e-01, -4.41924585e-01,  3.66792832e-01,\n",
       "        8.82759232e-02,  7.03797300e-01, -5.22994184e-01,  1.75513021e-01,\n",
       "       -7.39239764e-01, -9.79668857e-01, -1.30783899e-01,  1.03339273e+00,\n",
       "        7.33638622e-02, -4.81054813e-01, -6.85097348e-01,  1.89326017e-01,\n",
       "       -1.46676259e-01,  5.59536558e-01,  3.44770689e-01, -6.15734958e-01,\n",
       "        8.16582802e-03,  8.74651701e-01, -9.78289187e-02,  7.17696169e-01,\n",
       "       -6.38358888e-01,  8.78996355e-02,  3.75560596e-01, -2.76114385e-01,\n",
       "        1.75161025e-01, -5.77979489e-02,  2.57096733e-01, -9.45284844e-01,\n",
       "       -3.36117391e-01,  1.72585356e-01,  1.00841642e+00,  6.77425154e-01,\n",
       "       -4.07723904e-01, -1.48146648e-01, -2.37777275e-01,  8.29661239e-02,\n",
       "        2.75256079e-01,  7.21724035e-01,  4.38622969e-01,  3.18439491e-02,\n",
       "       -1.07979972e-01,  4.52877100e-01, -1.11014651e-02,  3.98651252e-01,\n",
       "       -1.00556919e+00,  3.61065002e-01,  1.32650259e-03,  1.28650720e-02,\n",
       "       -1.90608042e-01,  8.26013616e-02, -3.14032896e-01,  1.45643446e-01,\n",
       "        5.48098580e-01, -4.24203986e-01, -3.55762659e-01,  4.76025483e-01,\n",
       "       -6.88405508e-01, -6.00394717e-01, -1.26824184e-01,  9.59812605e-01,\n",
       "       -7.24676873e-01, -1.48339039e-01, -4.44906083e-01,  2.95182888e-01,\n",
       "       -6.61051555e-01, -4.10300073e-01, -1.18599320e+00,  4.33670055e-01,\n",
       "        2.70411955e-01, -4.62845499e-01,  4.21007588e-01,  8.94678426e-02,\n",
       "        3.12117056e-02,  1.83374887e-01,  7.45715746e-01, -1.91371073e-03,\n",
       "        2.39200689e-01, -3.02546999e-01,  5.06642655e-01,  3.47434349e-01,\n",
       "        5.24776367e-01,  2.19952725e-01, -1.25691604e+00,  5.95953464e-01,\n",
       "        2.89234448e-01, -1.57154042e-01, -1.06348390e+00,  9.26678018e-01,\n",
       "        1.78292514e-01, -1.01872192e-01,  3.93565413e-01,  8.14604177e-01,\n",
       "        8.53995602e-01,  1.37229046e-01,  5.80954560e-02,  2.20767891e-01,\n",
       "        1.69181998e-01,  1.56416832e-01,  3.21966767e-01,  4.64379439e-01,\n",
       "        2.79628210e-01,  9.71976513e-01, -7.16324910e-01,  4.61816869e-01,\n",
       "       -1.32877989e+00, -2.77511188e-01, -3.22043142e-01, -6.77012339e-03,\n",
       "        5.56018852e-01,  4.62333829e-01, -1.85784280e-01,  1.02971994e-01,\n",
       "        6.02284789e-01,  1.10940540e-01,  6.09239426e-01, -2.38282302e-01,\n",
       "       -1.26458997e+00,  5.64533958e-01, -2.02651708e-02, -7.18645002e-01,\n",
       "        2.06132234e-01,  3.39025795e-01, -4.16312939e-01,  4.42061725e-01,\n",
       "       -1.02875963e+00,  7.86255490e-01, -3.72784948e-01, -3.92730479e-02,\n",
       "        4.71845862e-01, -5.62723510e-01,  4.40871743e-01, -2.91521952e-01,\n",
       "        9.05629932e-02, -4.31812068e-01,  1.65005225e-02, -3.54832758e-01,\n",
       "       -7.37409373e-01, -1.05164394e-01, -7.06910020e-02,  1.60792952e-01,\n",
       "        6.64996955e-01,  2.30861351e-01,  9.62817631e-02, -1.28255635e-01,\n",
       "       -8.97037990e-02,  7.62142358e-01,  8.09484052e-01,  7.58107401e-02,\n",
       "        7.87298041e-02,  4.39161824e-01, -2.70070146e-01, -1.08908113e+00,\n",
       "        3.33956464e-02,  1.59990021e-01, -1.60882530e-01,  5.70931251e-01,\n",
       "        9.81956694e-01,  6.76545973e-01, -9.31021611e-01,  2.94193588e-01,\n",
       "        8.80192977e-02,  3.42561027e-01, -2.85933114e-01, -2.40774939e-01,\n",
       "       -6.75193125e-01,  3.40371279e-01, -6.36732215e-02, -4.86405372e-01,\n",
       "        1.93889372e-01,  6.23848916e-01,  4.78416882e-01, -4.69269541e-01,\n",
       "       -1.12166662e+00, -5.06132928e-01,  5.98165911e-03,  4.08002625e-01])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# MAP to musicvae latent space\n",
    "clusters[nearest[0]]\n",
    "musicvae_latent_encoding = tanh(np.sum([(a*random_gaussians[j]).dot(clusters[nearest[0]].T).T for j, a \n",
    "                                                        in enumerate(weights_of_transition)], axis=0)) * 2\n",
    "musicvae_latent_encoding"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8. Get midi and save to disk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ticks_per_quarter: 220\n",
      "tempos {\n",
      "  qpm: 120.0\n",
      "}\n",
      "notes {\n",
      "  pitch: 61\n",
      "  velocity: 80\n",
      "  start_time: 0.25\n",
      "  end_time: 0.5\n",
      "}\n",
      "notes {\n",
      "  pitch: 63\n",
      "  velocity: 80\n",
      "  start_time: 0.5\n",
      "  end_time: 0.75\n",
      "}\n",
      "notes {\n",
      "  pitch: 61\n",
      "  velocity: 80\n",
      "  start_time: 0.75\n",
      "  end_time: 1.0\n",
      "}\n",
      "notes {\n",
      "  pitch: 64\n",
      "  velocity: 80\n",
      "  start_time: 1.0\n",
      "  end_time: 1.25\n",
      "}\n",
      "notes {\n",
      "  pitch: 61\n",
      "  velocity: 80\n",
      "  start_time: 1.25\n",
      "  end_time: 1.5\n",
      "}\n",
      "notes {\n",
      "  pitch: 66\n",
      "  velocity: 80\n",
      "  start_time: 1.75\n",
      "  end_time: 2.0\n",
      "}\n",
      "total_time: 2.0\n",
      "\n",
      "WARNING:tensorflow:From d:\\captcha\\synvae\\lib\\site-packages\\magenta\\music\\midi_io.py:220: The name tf.gfile.Copy is deprecated. Please use tf.io.gfile.copy instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "midi_representation = net.decode(musicvae_latent_encoding.reshape(1, -1), length=16, temperature=0.001)[0]\n",
    "print(midi_representation)\n",
    "# saving midi to disk\n",
    "music.sequence_proto_to_midi_file(midi_representation,  \"test0.midi\")"
   ]
  }
 ],
 "metadata": {
  "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": 4
}