07_04_musegan_train.ipynb

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 "cells": [
  {
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   "metadata": {},
   "source": [
    "# MuseGAN Training"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import types\n",
    "\n",
    "from models.MuseGAN import MuseGAN\n",
    "from utils.loaders import load_music\n",
    "\n",
    "from music21 import midi\n",
    "from music21 import note, stream, duration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# run params\n",
    "SECTION = 'compose'\n",
    "RUN_ID = '001'\n",
    "DATA_NAME = 'chorales'\n",
    "FILENAME = 'Jsb16thSeparated.npz'\n",
    "RUN_FOLDER = 'run/{}/'.format(SECTION)\n",
    "RUN_FOLDER += '_'.join([RUN_ID, DATA_NAME])\n",
    "\n",
    "\n",
    "\n",
    "if not os.path.exists(RUN_FOLDER):\n",
    "    os.mkdir(RUN_FOLDER)\n",
    "    os.mkdir(os.path.join(RUN_FOLDER, 'viz'))\n",
    "    os.mkdir(os.path.join(RUN_FOLDER, 'images'))\n",
    "    os.mkdir(os.path.join(RUN_FOLDER, 'weights'))\n",
    "    os.mkdir(os.path.join(RUN_FOLDER, 'samples'))\n",
    "\n",
    "mode =  'build' # ' 'load' # "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "BATCH_SIZE = 64\n",
    "n_bars = 2\n",
    "n_steps_per_bar = 16\n",
    "n_pitches = 84\n",
    "n_tracks = 4\n",
    "\n",
    "data_binary, data_ints, raw_data = load_music(DATA_NAME, FILENAME, n_bars, n_steps_per_bar)\n",
    "data_binary = np.squeeze(data_binary)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## architecture"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "gan = MuseGAN(input_dim = data_binary.shape[1:]\n",
    "        , critic_learning_rate = 0.001\n",
    "        , generator_learning_rate = 0.001\n",
    "        , optimiser = 'adam'\n",
    "        , grad_weight = 10\n",
    "        , z_dim = 32\n",
    "        , batch_size = BATCH_SIZE\n",
    "        , n_tracks = n_tracks\n",
    "        , n_bars = n_bars\n",
    "        , n_steps_per_bar = n_steps_per_bar\n",
    "        , n_pitches = n_pitches\n",
    "        )\n",
    "\n",
    "if mode == 'build':\n",
    "    gan.save(RUN_FOLDER)\n",
    "else:                 \n",
    "    gan.load_weights(RUN_FOLDER)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "gan.chords_tempNetwork.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "gan.barGen[0].summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "gan.generator.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "gan.critic.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "EPOCHS = 6000\n",
    "PRINT_EVERY_N_BATCHES = 10\n",
    "\n",
    "gan.epoch = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "gan.train(     \n",
    "    data_binary\n",
    "    , batch_size = BATCH_SIZE\n",
    "    , epochs = EPOCHS\n",
    "    , run_folder = RUN_FOLDER\n",
    "    , print_every_n_batches = PRINT_EVERY_N_BATCHES\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = plt.figure()\n",
    "plt.plot([x[0] for x in gan.d_losses], color='black', linewidth=0.25)\n",
    "\n",
    "plt.plot([x[1] for x in gan.d_losses], color='green', linewidth=0.25)\n",
    "plt.plot([x[2] for x in gan.d_losses], color='red', linewidth=0.25)\n",
    "plt.plot(gan.g_losses, color='orange', linewidth=0.25)\n",
    "\n",
    "plt.xlabel('batch', fontsize=18)\n",
    "plt.ylabel('loss', fontsize=16)\n",
    "\n",
    "plt.xlim(0, len(gan.d_losses))\n",
    "# plt.ylim(0, 2)\n",
    "\n",
    "plt.show()\n"
   ]
  }
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