rnn.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setup and load data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "use_pca = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['ffmpeg', 'ffmpeg_file', 'imagemagick', 'imagemagick_file', 'html']\n"
     ]
    }
   ],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "from functions.functions import *\n",
    "from functions.plotting import *\n",
    "from functions.autoencoder i\n",
    "mport *\n",
    "from functions.mdn import *\n",
    "from keras import backend as K\n",
    "from keras.callbacks import TerminateOnNaN, ModelCheckpoint\n",
    "from keras.models import model_from_json\n",
    "from livelossplot.keras import PlotLossesCallback\n",
    "import livelossplot\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import os\n",
    "import glob\n",
    "from itertools import combinations\n",
    "import matplotlib\n",
    "plt.rcParams['animation.ffmpeg_path'] = '/project/hep/demers/mnp3/miniconda3/envs/choreo/bin/ffmpeg' # for using html5 video in Jupyter notebook\n",
    "print(matplotlib.animation.writers.list()) # check that ffmpeg is loaded. if it's not there, use .to_jshtml() instead of .to_html5_video()."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GPUs found: 0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "()"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "setup_gpus()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files loaded: ['rnn_data/mariel_betternot_and_retrograde.npy', 'rnn_data/mariel_beyond.npy', 'rnn_data/mariel_chunli.npy', 'rnn_data/mariel_honey.npy', 'rnn_data/mariel_knownbetter.npy', 'rnn_data/mariel_penelope.npy']\n"
     ]
    }
   ],
   "source": [
    "data = load_data('rnn_data/mariel_*')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Check out the real data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting from frame 20137...\n"
     ]
    },
    {
     "data": {
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       "AAAAAExhdmY1OC4yMC4xMDA=\n",
       "\">\n",
       "  Your browser does not support the video tag.\n",
       "</video>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame = np.random.randint(0,data.full.X.shape[1]-50)\n",
    "print(\"Starting from frame {}...\".format(frame))\n",
    "\n",
    "# HTML(animate(data.full.X[:,frame:,:], frames=100))\n",
    "HTML(animate(data.selected.X[:,frame:,:], frames=100, edges=data.selected.edges, colors='black'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LSTM + MDN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the data with x,y centering:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "MA==\n",
       "\">\n",
       "  Your browser does not support the video tag.\n",
       "</video>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame = np.random.randint(0,data.full.X.shape[1]-50)\n",
    "X = data.selected.X # 15 joints\n",
    "X = X.swapaxes(0, 1)\n",
    "X[:,:,0] = X[:,:,0] - np.mean(X[:,:,0], axis=0) + 0.5*np.ones(15)\n",
    "X[:,:,1] = X[:,:,1] - np.mean(X[:,:,1], axis=0) + 0.5*np.ones(15)\n",
    "HTML(animate(data.selected.X[:,frame:,:], frames=100, edges=data.selected.edges, colors='black'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(44299, 10, 45)\n",
      "(44299, 45)\n",
      "Model: \"model_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         (None, 10, 45)            0         \n",
      "_________________________________________________________________\n",
      "lstm_1 (LSTM)                (None, 10, 32)            9984      \n",
      "_________________________________________________________________\n",
      "lstm_2 (LSTM)                (None, 10, 32)            8320      \n",
      "_________________________________________________________________\n",
      "lstm_3 (LSTM)                (None, 32)                8320      \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 32)                1056      \n",
      "_________________________________________________________________\n",
      "mdn_1 (MDN)                  (None, 32)                4653      \n",
      "=================================================================\n",
      "Total params: 32,333\n",
      "Trainable params: 32,333\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "if use_pca: \n",
    "    X = data.selected.X # 15 joints\n",
    "    # Average frame-by-frame in (x,y):\n",
    "    X = X.swapaxes(0, 1)\n",
    "    X[:,:,0] = X[:,:,0] - np.mean(X[:,:,0], axis=0) + 0.5*np.ones(15)\n",
    "    X[:,:,1] = X[:,:,1] - np.mean(X[:,:,1], axis=0) + 0.5*np.ones(15)\n",
    "    X = X.reshape(X.shape[0],X.shape[1]*X.shape[2])\n",
    "\n",
    "    # PCA time:\n",
    "    from sklearn.decomposition import PCA\n",
    "    pca = PCA(.95)\n",
    "    pca_reduced_data = pca.fit_transform(X)\n",
    "    print('PCA reduction to a {}-dimensional latent space.'.format(pca_reduced_data.shape[1]))\n",
    "    print('Explained variation per principal component: {}'.format(pca.explained_variance_ratio_))\n",
    "    print(pca_reduced_data.shape)\n",
    "    n_time, n_dims, n_verts  = pca_reduced_data.shape[0], 1, pca_reduced_data.shape[1]\n",
    "    n_mixes = 6\n",
    "    look_back = 10\n",
    "\n",
    "    lstm_mdn = LSTM_MDN(cells = [32,32,32,32], n_verts=n_verts, n_dims=n_dims, n_mixes=n_mixes, look_back=look_back)\n",
    "\n",
    "    train_X = []\n",
    "    train_Y = []\n",
    "    for i in range(look_back, n_time, 1):\n",
    "        train_X.append( pca_reduced_data[i-look_back:i,:] ) # look_back, verts * dims\n",
    "        train_Y.append( pca_reduced_data[i,:] ) # verts * dims\n",
    "    train_X = np.array(train_X) # n_samples, lookback, verts * dims\n",
    "    train_Y = np.array(train_Y) # n_samples, verts * dims\n",
    "    \n",
    "    print(train_X.shape, train_Y.shape)\n",
    "    lstm_mdn.model.summary()\n",
    "    \n",
    "else:\n",
    "    X = data.selected.X # 15 joints\n",
    "    n_verts, n_time, n_dims = X.shape\n",
    "    n_mixes = 3\n",
    "    look_back = 10\n",
    "\n",
    "    lstm_mdn = LSTM_MDN(cells = [32,32,32,32], n_verts=n_verts, n_dims=n_dims, n_mixes=n_mixes, look_back=look_back)\n",
    "    train_X, train_Y = lstm_mdn.prepare_inputs(X, look_back=look_back)\n",
    "    print(train_X.shape)\n",
    "    print(train_Y.shape)\n",
    "    lstm_mdn.model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save the model architecture: \n",
    "model_json = lstm_mdn.model.to_json()\n",
    "with open(\"weights/model-lstm.json\", \"w\") as json_file:\n",
    "    json_file.write(model_json)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/200\n",
      "44299/44299 [==============================] - 9s 210us/step - loss: -1.8052 - accuracy: 0.1312\n",
      "\n",
      "Epoch 00001: accuracy improved from -inf to 0.13118, saving model to weights/weights-lstm.h5\n",
      "Epoch 2/200\n",
      "44299/44299 [==============================] - 9s 208us/step - loss: -1.8326 - accuracy: 0.0179\n",
      "\n",
      "Epoch 00002: accuracy did not improve from 0.13118\n",
      "Epoch 3/200\n",
      "44299/44299 [==============================] - 9s 208us/step - loss: -1.8812 - accuracy: 0.0809\n",
      "\n",
      "Epoch 00003: accuracy did not improve from 0.13118\n",
      "Epoch 4/200\n",
      "44299/44299 [==============================] - 9s 208us/step - loss: -1.8410 - accuracy: 0.0527\n",
      "\n",
      "Epoch 00004: accuracy did not improve from 0.13118\n",
      "Epoch 5/200\n",
      "44299/44299 [==============================] - 9s 209us/step - loss: -1.8351 - accuracy: 0.0573\n",
      "\n",
      "Epoch 00005: accuracy did not improve from 0.13118\n",
      "Epoch 6/200\n",
      "44299/44299 [==============================] - 9s 207us/step - loss: -1.8713 - accuracy: 0.0814\n",
      "\n",
      "Epoch 00006: accuracy did not improve from 0.13118\n",
      "Epoch 7/200\n",
      "44299/44299 [==============================] - 9s 207us/step - loss: -1.9230 - accuracy: 0.1128\n",
      "\n",
      "Epoch 00007: accuracy did not improve from 0.13118\n",
      "Epoch 8/200\n",
      "44299/44299 [==============================] - 9s 208us/step - loss: -1.8803 - accuracy: 0.1050\n",
      "\n",
      "Epoch 00008: accuracy did not improve from 0.13118\n",
      "Epoch 9/200\n",
      "44299/44299 [==============================] - 9s 207us/step - loss: -1.9180 - accuracy: 0.1025\n",
      "\n",
      "Epoch 00009: accuracy did not improve from 0.13118\n",
      "Epoch 10/200\n",
      "44299/44299 [==============================] - 9s 208us/step - loss: -1.8674 - accuracy: 0.0985\n",
      "\n",
      "Epoch 00010: accuracy did not improve from 0.13118\n",
      "Epoch 11/200\n",
      "44299/44299 [==============================] - 9s 210us/step - loss: -1.9673 - accuracy: 0.0829\n",
      "\n",
      "Epoch 00011: accuracy did not improve from 0.13118\n",
      "Epoch 12/200\n",
      "44299/44299 [==============================] - 9s 210us/step - loss: -1.9649 - accuracy: 0.1114\n",
      "\n",
      "Epoch 00012: accuracy did not improve from 0.13118\n",
      "Epoch 13/200\n",
      "44299/44299 [==============================] - 9s 208us/step - loss: -2.0263 - accuracy: 0.1586\n",
      "\n",
      "Epoch 00013: accuracy improved from 0.13118 to 0.15858, saving model to weights/weights-lstm.h5\n",
      "Epoch 14/200\n",
      "44299/44299 [==============================] - 9s 210us/step - loss: -1.9492 - accuracy: 0.0702\n",
      "\n",
      "Epoch 00014: accuracy did not improve from 0.15858\n",
      "Epoch 15/200\n",
      "44299/44299 [==============================] - 9s 210us/step - loss: -1.9628 - accuracy: 0.1995\n",
      "\n",
      "Epoch 00015: accuracy improved from 0.15858 to 0.19953, saving model to weights/weights-lstm.h5\n",
      "Epoch 16/200\n",
      "44299/44299 [==============================] - 9s 210us/step - loss: -2.0317 - accuracy: 0.1700\n",
      "\n",
      "Epoch 00016: accuracy did not improve from 0.19953\n",
      "Epoch 17/200\n",
      "44299/44299 [==============================] - 9s 210us/step - loss: -1.9832 - accuracy: 0.1120\n",
      "\n",
      "Epoch 00017: accuracy did not improve from 0.19953\n",
      "Epoch 18/200\n",
      "44299/44299 [==============================] - 9s 209us/step - loss: -2.0624 - accuracy: 0.1425\n",
      "\n",
      "Epoch 00018: accuracy did not improve from 0.19953\n",
      "Epoch 19/200\n",
      "44299/44299 [==============================] - 9s 208us/step - loss: -1.9927 - accuracy: 0.1170\n",
      "\n",
      "Epoch 00019: accuracy did not improve from 0.19953\n",
      "Epoch 20/200\n",
      "44299/44299 [==============================] - 9s 209us/step - loss: -1.9968 - accuracy: 0.1218\n",
      "\n",
      "Epoch 00020: accuracy did not improve from 0.19953\n",
      "Epoch 21/200\n",
      "44299/44299 [==============================] - 9s 209us/step - loss: -2.0637 - accuracy: 0.1330\n",
      "\n",
      "Epoch 00021: accuracy did not improve from 0.19953\n",
      "Epoch 22/200\n",
      "44299/44299 [==============================] - 9s 208us/step - loss: -2.0029 - accuracy: 0.1403\n",
      "\n",
      "Epoch 00022: accuracy did not improve from 0.19953\n",
      "Epoch 23/200\n",
      "44299/44299 [==============================] - 9s 208us/step - loss: -2.0929 - accuracy: 0.1708\n",
      "\n",
      "Epoch 00023: accuracy did not improve from 0.19953\n",
      "Epoch 24/200\n",
      "44299/44299 [==============================] - 9s 208us/step - loss: -2.0676 - accuracy: 0.2028\n",
      "\n",
      "Epoch 00024: accuracy improved from 0.19953 to 0.20278, saving model to weights/weights-lstm.h5\n",
      "Epoch 25/200\n",
      "44299/44299 [==============================] - 9s 207us/step - loss: -2.1095 - accuracy: 0.1646\n",
      "\n",
      "Epoch 00025: accuracy did not improve from 0.20278\n",
      "Epoch 26/200\n",
      "44299/44299 [==============================] - 9s 207us/step - loss: -2.0781 - accuracy: 0.1909\n",
      "\n",
      "Epoch 00026: accuracy did not improve from 0.20278\n",
      "Epoch 27/200\n",
      "44299/44299 [==============================] - 9s 210us/step - loss: -2.1126 - accuracy: 0.1976\n",
      "\n",
      "Epoch 00027: accuracy did not improve from 0.20278\n",
      "Epoch 28/200\n",
      "44299/44299 [==============================] - 9s 210us/step - loss: -2.0733 - accuracy: 0.1346\n",
      "\n",
      "Epoch 00028: accuracy did not improve from 0.20278\n",
      "Epoch 29/200\n",
      "44299/44299 [==============================] - 9s 207us/step - loss: -2.2249 - accuracy: 0.1577\n",
      "\n",
      "Epoch 00029: accuracy did not improve from 0.20278\n",
      "Epoch 30/200\n",
      "44299/44299 [==============================] - 9s 208us/step - loss: -2.1154 - accuracy: 0.1371\n",
      "\n",
      "Epoch 00030: accuracy did not improve from 0.20278\n",
      "Epoch 31/200\n",
      "44299/44299 [==============================] - 9s 208us/step - loss: -2.1619 - accuracy: 0.1820\n",
      "\n",
      "Epoch 00031: accuracy did not improve from 0.20278\n",
      "Epoch 32/200\n",
      "44299/44299 [==============================] - 9s 209us/step - loss: -2.1333 - accuracy: 0.2317\n",
      "\n",
      "Epoch 00032: accuracy improved from 0.20278 to 0.23170, saving model to weights/weights-lstm.h5\n",
      "Epoch 33/200\n",
      "44299/44299 [==============================] - 9s 208us/step - loss: -2.1253 - accuracy: 0.1516\n",
      "\n",
      "Epoch 00033: accuracy did not improve from 0.23170\n",
      "Epoch 34/200\n",
      "44299/44299 [==============================] - 9s 208us/step - loss: -2.2060 - accuracy: 0.0517\n",
      "\n",
      "Epoch 00034: accuracy did not improve from 0.23170\n",
      "Epoch 35/200\n",
      "44299/44299 [==============================] - 9s 207us/step - loss: -2.2088 - accuracy: 0.1946\n",
      "\n",
      "Epoch 00035: accuracy did not improve from 0.23170\n",
      "Epoch 36/200\n",
      "44299/44299 [==============================] - 9s 207us/step - loss: -2.1086 - accuracy: 0.1848\n",
      "\n",
      "Epoch 00036: accuracy did not improve from 0.23170\n",
      "Epoch 37/200\n",
      "44299/44299 [==============================] - 9s 208us/step - loss: -2.1318 - accuracy: 0.1544\n",
      "\n",
      "Epoch 00037: accuracy did not improve from 0.23170\n",
      "Epoch 38/200\n",
      "44299/44299 [==============================] - 9s 209us/step - loss: -2.1814 - accuracy: 0.1168\n",
      "\n",
      "Epoch 00038: accuracy did not improve from 0.23170\n",
      "Epoch 39/200\n",
      "44299/44299 [==============================] - 9s 207us/step - loss: -2.2653 - accuracy: 0.1226\n",
      "\n",
      "Epoch 00039: accuracy did not improve from 0.23170\n",
      "Epoch 40/200\n",
      "44299/44299 [==============================] - 9s 210us/step - loss: -2.1462 - accuracy: 0.1721\n",
      "\n",
      "Epoch 00040: accuracy did not improve from 0.23170\n",
      "Epoch 41/200\n",
      "44299/44299 [==============================] - 9s 208us/step - loss: -2.3167 - accuracy: 0.1950\n",
      "\n",
      "Epoch 00041: accuracy did not improve from 0.23170\n",
      "Epoch 42/200\n",
      "44299/44299 [==============================] - 9s 209us/step - loss: -2.2272 - accuracy: 0.0828\n",
      "\n",
      "Epoch 00042: accuracy did not improve from 0.23170\n",
      "Epoch 43/200\n",
      "44299/44299 [==============================] - 9s 210us/step - loss: -2.1946 - accuracy: 0.1039\n",
      "\n",
      "Epoch 00043: accuracy did not improve from 0.23170\n",
      "Epoch 44/200\n",
      "44299/44299 [==============================] - 9s 210us/step - loss: -2.2586 - accuracy: 0.1698\n",
      "\n",
      "Epoch 00044: accuracy did not improve from 0.23170\n",
      "Epoch 45/200\n",
      "44299/44299 [==============================] - 9s 209us/step - loss: -2.3026 - accuracy: 0.1519\n",
      "\n",
      "Epoch 00045: accuracy did not improve from 0.23170\n",
      "Epoch 46/200\n",
      "44299/44299 [==============================] - 9s 209us/step - loss: -2.2426 - accuracy: 0.1350\n",
      "\n",
      "Epoch 00046: accuracy did not improve from 0.23170\n",
      "Epoch 47/200\n",
      "44299/44299 [==============================] - 9s 211us/step - loss: -2.1993 - accuracy: 0.2171\n",
      "\n",
      "Epoch 00047: accuracy did not improve from 0.23170\n",
      "Epoch 48/200\n",
      "44299/44299 [==============================] - 9s 210us/step - loss: -2.2653 - accuracy: 0.1597\n",
      "\n",
      "Epoch 00048: accuracy did not improve from 0.23170\n",
      "Epoch 49/200\n",
      "44299/44299 [==============================] - 9s 210us/step - loss: -2.2643 - accuracy: 0.0767\n",
      "\n",
      "Epoch 00049: accuracy did not improve from 0.23170\n",
      "Epoch 50/200\n",
      "44299/44299 [==============================] - 9s 209us/step - loss: -2.2427 - accuracy: 0.1271\n",
      "\n",
      "Epoch 00050: accuracy did not improve from 0.23170\n",
      "Epoch 51/200\n",
      "44299/44299 [==============================] - 9s 209us/step - loss: -2.2615 - accuracy: 0.1322\n",
      "\n",
      "Epoch 00051: accuracy did not improve from 0.23170\n",
      "Epoch 52/200\n",
      "44299/44299 [==============================] - 9s 209us/step - loss: -2.2409 - accuracy: 0.1156\n",
      "\n",
      "Epoch 00052: accuracy did not improve from 0.23170\n",
      "Epoch 53/200\n",
      "44299/44299 [==============================] - 9s 209us/step - loss: -2.2675 - accuracy: 0.2127\n",
      "\n",
      "Epoch 00053: accuracy did not improve from 0.23170\n",
      "Epoch 54/200\n",
      "44299/44299 [==============================] - 9s 210us/step - loss: -2.3710 - accuracy: 0.1841\n",
      "\n",
      "Epoch 00054: accuracy did not improve from 0.23170\n",
      "Epoch 55/200\n",
      "44299/44299 [==============================] - 9s 208us/step - loss: -2.3194 - accuracy: 0.1633\n",
      "\n",
      "Epoch 00055: accuracy did not improve from 0.23170\n",
      "Epoch 56/200\n",
      "44299/44299 [==============================] - 9s 210us/step - loss: -2.2606 - accuracy: 0.1562\n",
      "\n",
      "Epoch 00056: accuracy did not improve from 0.23170\n",
      "Epoch 57/200\n",
      "44299/44299 [==============================] - 9s 209us/step - loss: -2.3241 - accuracy: 0.1400\n",
      "\n",
      "Epoch 00057: accuracy did not improve from 0.23170\n",
      "Epoch 58/200\n",
      "44299/44299 [==============================] - 9s 212us/step - loss: -2.3034 - accuracy: 0.1152\n",
      "\n",
      "Epoch 00058: accuracy did not improve from 0.23170\n",
      "Epoch 59/200\n",
      "44299/44299 [==============================] - 9s 209us/step - loss: -2.3046 - accuracy: 0.1938\n",
      "\n",
      "Epoch 00059: accuracy did not improve from 0.23170\n",
      "Epoch 60/200\n",
      "44299/44299 [==============================] - 9s 208us/step - loss: -2.3065 - accuracy: 0.1278\n",
      "\n",
      "Epoch 00060: accuracy did not improve from 0.23170\n",
      "Epoch 61/200\n",
      "44299/44299 [==============================] - 9s 207us/step - loss: -2.3628 - accuracy: 0.1026\n",
      "\n",
      "Epoch 00061: accuracy did not improve from 0.23170\n",
      "Epoch 62/200\n",
      "44299/44299 [==============================] - 9s 210us/step - loss: -2.2891 - accuracy: 0.1656\n",
      "\n",
      "Epoch 00062: accuracy did not improve from 0.23170\n",
      "Epoch 63/200\n",
      "44299/44299 [==============================] - 9s 210us/step - loss: -2.3301 - accuracy: 0.0900\n",
      "\n",
      "Epoch 00063: accuracy did not improve from 0.23170\n",
      "Epoch 64/200\n",
      "44299/44299 [==============================] - 9s 211us/step - loss: -2.3177 - accuracy: 0.1013\n",
      "\n",
      "Epoch 00064: accuracy did not improve from 0.23170\n",
      "Epoch 65/200\n",
      "44299/44299 [==============================] - 9s 208us/step - loss: -2.3128 - accuracy: 0.1325\n",
      "\n",
      "Epoch 00065: accuracy did not improve from 0.23170\n",
      "Epoch 66/200\n",
      "44299/44299 [==============================] - 9s 209us/step - loss: -2.3936 - accuracy: 0.1219\n",
      "\n",
      "Epoch 00066: accuracy did not improve from 0.23170\n",
      "Epoch 67/200\n",
      "44299/44299 [==============================] - 9s 210us/step - loss: -2.3807 - accuracy: 0.1320\n",
      "\n",
      "Epoch 00067: accuracy did not improve from 0.23170\n",
      "Epoch 68/200\n",
      "44299/44299 [==============================] - 9s 209us/step - loss: -2.3536 - accuracy: 0.1218\n",
      "\n",
      "Epoch 00068: accuracy did not improve from 0.23170\n",
      "Epoch 69/200\n",
      "44299/44299 [==============================] - 9s 210us/step - loss: -2.2930 - accuracy: 0.1485\n",
      "\n",
      "Epoch 00069: accuracy did not improve from 0.23170\n",
      "Epoch 70/200\n",
      "44299/44299 [==============================] - 10s 232us/step - loss: -2.3531 - accuracy: 0.0197\n",
      "\n",
      "Epoch 00070: accuracy did not improve from 0.23170\n",
      "Epoch 71/200\n",
      "44299/44299 [==============================] - 11s 237us/step - loss: -2.3749 - accuracy: 0.1225\n",
      "\n",
      "Epoch 00071: accuracy did not improve from 0.23170\n",
      "Epoch 72/200\n",
      "44299/44299 [==============================] - 11s 247us/step - loss: -2.3377 - accuracy: 0.1194\n",
      "\n",
      "Epoch 00072: accuracy did not improve from 0.23170\n",
      "Epoch 73/200\n",
      "44299/44299 [==============================] - 10s 228us/step - loss: -2.2594 - accuracy: 0.0526\n",
      "\n",
      "Epoch 00073: accuracy did not improve from 0.23170\n",
      "Epoch 74/200\n",
      "44299/44299 [==============================] - 9s 210us/step - loss: -2.3670 - accuracy: 0.0986\n",
      "\n",
      "Epoch 00074: accuracy did not improve from 0.23170\n",
      "Epoch 75/200\n",
      "44299/44299 [==============================] - 10s 216us/step - loss: -2.3761 - accuracy: 0.0957\n",
      "\n",
      "Epoch 00075: accuracy did not improve from 0.23170\n",
      "Epoch 76/200\n",
      "44299/44299 [==============================] - 9s 211us/step - loss: -2.3728 - accuracy: 0.1189\n",
      "\n",
      "Epoch 00076: accuracy did not improve from 0.23170\n",
      "Epoch 77/200\n",
      "44299/44299 [==============================] - 9s 210us/step - loss: -2.4303 - accuracy: 0.1125\n",
      "\n",
      "Epoch 00077: accuracy did not improve from 0.23170\n",
      "Epoch 78/200\n",
      "44299/44299 [==============================] - 9s 210us/step - loss: -2.4062 - accuracy: 0.1131\n",
      "\n",
      "Epoch 00078: accuracy did not improve from 0.23170\n",
      "Epoch 79/200\n",
      "44299/44299 [==============================] - 9s 210us/step - loss: -2.4073 - accuracy: 0.1318\n",
      "\n",
      "Epoch 00079: accuracy did not improve from 0.23170\n",
      "Epoch 80/200\n",
      "44299/44299 [==============================] - 9s 208us/step - loss: -2.4199 - accuracy: 0.1798\n",
      "\n",
      "Epoch 00080: accuracy did not improve from 0.23170\n",
      "Epoch 81/200\n",
      "44299/44299 [==============================] - 9s 208us/step - loss: -2.3615 - accuracy: 0.0698\n",
      "\n",
      "Epoch 00081: accuracy did not improve from 0.23170\n",
      "Epoch 82/200\n",
      "44299/44299 [==============================] - 9s 210us/step - loss: -2.3636 - accuracy: 0.0638\n",
      "\n",
      "Epoch 00082: accuracy did not improve from 0.23170\n",
      "Epoch 83/200\n",
      "44299/44299 [==============================] - 9s 209us/step - loss: -2.3900 - accuracy: 0.0504\n",
      "\n",
      "Epoch 00083: accuracy did not improve from 0.23170\n",
      "Epoch 84/200\n",
      "44299/44299 [==============================] - 9s 209us/step - loss: -2.3877 - accuracy: 0.1167\n",
      "\n",
      "Epoch 00084: accuracy did not improve from 0.23170\n",
      "Epoch 85/200\n",
      "44299/44299 [==============================] - 9s 210us/step - loss: -2.4242 - accuracy: 0.0604\n",
      "\n",
      "Epoch 00085: accuracy did not improve from 0.23170\n",
      "Epoch 86/200\n",
      "44299/44299 [==============================] - 9s 211us/step - loss: -2.4891 - accuracy: 0.1172\n",
      "\n",
      "Epoch 00086: accuracy did not improve from 0.23170\n",
      "Epoch 87/200\n",
      "44299/44299 [==============================] - 9s 210us/step - loss: -2.4277 - accuracy: 0.1304\n",
      "\n",
      "Epoch 00087: accuracy did not improve from 0.23170\n",
      "Epoch 88/200\n",
      "44299/44299 [==============================] - 9s 209us/step - loss: -2.4304 - accuracy: 0.1185\n",
      "\n",
      "Epoch 00088: accuracy did not improve from 0.23170\n",
      "Epoch 89/200\n",
      "44299/44299 [==============================] - 9s 208us/step - loss: -2.5294 - accuracy: 0.1333\n",
      "\n",
      "Epoch 00089: accuracy did not improve from 0.23170\n",
      "Epoch 90/200\n",
      "44299/44299 [==============================] - 9s 208us/step - loss: -2.4154 - accuracy: 0.1255\n",
      "\n",
      "Epoch 00090: accuracy did not improve from 0.23170\n",
      "Epoch 91/200\n",
      "44299/44299 [==============================] - 9s 209us/step - loss: -2.4869 - accuracy: 0.1406\n",
      "\n",
      "Epoch 00091: accuracy did not improve from 0.23170\n",
      "Epoch 92/200\n",
      "44299/44299 [==============================] - 9s 208us/step - loss: -2.4454 - accuracy: 0.1692\n",
      "\n",
      "Epoch 00092: accuracy did not improve from 0.23170\n",
      "Epoch 93/200\n",
      "44299/44299 [==============================] - 9s 207us/step - loss: -2.5331 - accuracy: 0.1635\n",
      "\n",
      "Epoch 00093: accuracy did not improve from 0.23170\n",
      "Epoch 94/200\n",
      "44299/44299 [==============================] - 9s 207us/step - loss: -2.5116 - accuracy: 0.0916\n",
      "\n",
      "Epoch 00094: accuracy did not improve from 0.23170\n",
      "Epoch 95/200\n",
      "44299/44299 [==============================] - 9s 208us/step - loss: -2.4583 - accuracy: 0.1049\n",
      "\n",
      "Epoch 00095: accuracy did not improve from 0.23170\n",
      "Epoch 96/200\n",
      "44299/44299 [==============================] - 9s 209us/step - loss: -2.4981 - accuracy: 0.1296\n",
      "\n",
      "Epoch 00096: accuracy did not improve from 0.23170\n",
      "Epoch 97/200\n",
      "44299/44299 [==============================] - 9s 207us/step - loss: -2.5826 - accuracy: 0.1802\n",
      "\n",
      "Epoch 00097: accuracy did not improve from 0.23170\n",
      "Epoch 98/200\n",
      "44299/44299 [==============================] - 9s 207us/step - loss: -2.4866 - accuracy: 0.1551\n",
      "\n",
      "Epoch 00098: accuracy did not improve from 0.23170\n",
      "Epoch 99/200\n",
      "44299/44299 [==============================] - 9s 208us/step - loss: -2.6053 - accuracy: 0.2159\n",
      "\n",
      "Epoch 00099: accuracy did not improve from 0.23170\n",
      "Epoch 100/200\n",
      "44299/44299 [==============================] - 9s 208us/step - loss: -2.5501 - accuracy: 0.1986\n",
      "\n",
      "Epoch 00100: accuracy did not improve from 0.23170\n",
      "Epoch 101/200\n",
      "44299/44299 [==============================] - 9s 209us/step - loss: -2.5173 - accuracy: 0.1195\n",
      "\n",
      "Epoch 00101: accuracy did not improve from 0.23170\n",
      "Epoch 102/200\n",
      "44299/44299 [==============================] - 9s 207us/step - loss: -2.4558 - accuracy: 0.1122\n",
      "\n",
      "Epoch 00102: accuracy did not improve from 0.23170\n",
      "Epoch 103/200\n",
      "44299/44299 [==============================] - 9s 209us/step - loss: -2.5690 - accuracy: 0.1779\n",
      "\n",
      "Epoch 00103: accuracy did not improve from 0.23170\n",
      "Epoch 104/200\n",
      "44299/44299 [==============================] - 9s 208us/step - loss: -2.4535 - accuracy: 0.2189\n",
      "\n",
      "Epoch 00104: accuracy did not improve from 0.23170\n",
      "Epoch 105/200\n",
      "44299/44299 [==============================] - 9s 208us/step - loss: -2.5659 - accuracy: 0.1158\n",
      "\n",
      "Epoch 00105: accuracy did not improve from 0.23170\n",
      "Epoch 106/200\n",
      "44299/44299 [==============================] - 9s 209us/step - loss: -2.5545 - accuracy: 0.1554\n",
      "\n",
      "Epoch 00106: accuracy did not improve from 0.23170\n",
      "Epoch 107/200\n",
      "44299/44299 [==============================] - 9s 209us/step - loss: -2.5399 - accuracy: 0.1885\n",
      "\n",
      "Epoch 00107: accuracy did not improve from 0.23170\n",
      "Epoch 108/200\n",
      "44299/44299 [==============================] - 9s 210us/step - loss: -2.5722 - accuracy: 0.1700\n",
      "\n",
      "Epoch 00108: accuracy did not improve from 0.23170\n",
      "Epoch 109/200\n",
      "44299/44299 [==============================] - 9s 207us/step - loss: -2.4721 - accuracy: 0.1742\n",
      "\n",
      "Epoch 00109: accuracy did not improve from 0.23170\n",
      "Epoch 110/200\n",
      "44299/44299 [==============================] - 9s 208us/step - loss: -2.6370 - accuracy: 0.2093\n",
      "\n",
      "Epoch 00110: accuracy did not improve from 0.23170\n",
      "Epoch 111/200\n",
      "44299/44299 [==============================] - 9s 208us/step - loss: -2.5631 - accuracy: 0.2055\n",
      "\n",
      "Epoch 00111: accuracy did not improve from 0.23170\n",
      "Epoch 112/200\n",
      "44299/44299 [==============================] - 9s 209us/step - loss: -2.5265 - accuracy: 0.1868\n",
      "\n",
      "Epoch 00112: accuracy did not improve from 0.23170\n",
      "Epoch 113/200\n",
      "44299/44299 [==============================] - 9s 208us/step - loss: -2.6264 - accuracy: 0.2110\n",
      "\n",
      "Epoch 00113: accuracy did not improve from 0.23170\n",
      "Epoch 114/200\n",
      "44299/44299 [==============================] - 9s 209us/step - loss: -2.5534 - accuracy: 0.2354\n",
      "\n",
      "Epoch 00114: accuracy improved from 0.23170 to 0.23542, saving model to weights/weights-lstm.h5\n",
      "Epoch 115/200\n",
      "44299/44299 [==============================] - 9s 209us/step - loss: -2.6519 - accuracy: 0.1016\n",
      "\n",
      "Epoch 00115: accuracy did not improve from 0.23542\n",
      "Epoch 116/200\n",
      "44299/44299 [==============================] - 9s 209us/step - loss: -2.5050 - accuracy: 0.1591\n",
      "\n",
      "Epoch 00116: accuracy did not improve from 0.23542\n",
      "Epoch 117/200\n",
      "44299/44299 [==============================] - 9s 211us/step - loss: -2.5949 - accuracy: 0.1828\n",
      "\n",
      "Epoch 00117: accuracy did not improve from 0.23542\n",
      "Epoch 118/200\n",
      "44299/44299 [==============================] - 9s 211us/step - loss: -2.5820 - accuracy: 0.2662\n",
      "\n",
      "Epoch 00118: accuracy improved from 0.23542 to 0.26617, saving model to weights/weights-lstm.h5\n",
      "Epoch 119/200\n",
      "44299/44299 [==============================] - 10s 237us/step - loss: -2.6365 - accuracy: 0.2639\n",
      "\n",
      "Epoch 00119: accuracy did not improve from 0.26617\n",
      "Epoch 120/200\n",
      "44299/44299 [==============================] - 9s 208us/step - loss: -2.5658 - accuracy: 0.2058\n",
      "\n",
      "Epoch 00120: accuracy did not improve from 0.26617\n",
      "Epoch 121/200\n",
      "44299/44299 [==============================] - 9s 208us/step - loss: -2.5624 - accuracy: 0.2259\n",
      "\n",
      "Epoch 00121: accuracy did not improve from 0.26617\n",
      "Epoch 122/200\n",
      "44299/44299 [==============================] - 9s 209us/step - loss: -2.5961 - accuracy: 0.2205\n",
      "\n",
      "Epoch 00122: accuracy did not improve from 0.26617\n",
      "Epoch 123/200\n",
      "44299/44299 [==============================] - 9s 207us/step - loss: -2.5382 - accuracy: 0.2127\n",
      "\n",
      "Epoch 00123: accuracy did not improve from 0.26617\n",
      "Epoch 124/200\n",
      "44299/44299 [==============================] - 9s 208us/step - loss: -2.6080 - accuracy: 0.2161\n",
      "\n",
      "Epoch 00124: accuracy did not improve from 0.26617\n",
      "Epoch 125/200\n",
      "44299/44299 [==============================] - 9s 208us/step - loss: -2.6319 - accuracy: 0.2474\n",
      "\n",
      "Epoch 00125: accuracy did not improve from 0.26617\n",
      "Epoch 126/200\n",
      "44299/44299 [==============================] - 9s 209us/step - loss: -2.6361 - accuracy: 0.2086\n",
      "\n",
      "Epoch 00126: accuracy did not improve from 0.26617\n",
      "Epoch 127/200\n",
      "44299/44299 [==============================] - 9s 209us/step - loss: -2.6850 - accuracy: 0.2528\n",
      "\n",
      "Epoch 00127: accuracy did not improve from 0.26617\n",
      "Epoch 128/200\n",
      "44299/44299 [==============================] - 9s 209us/step - loss: -2.6300 - accuracy: 0.2389\n",
      "\n",
      "Epoch 00128: accuracy did not improve from 0.26617\n",
      "Epoch 129/200\n",
      "44299/44299 [==============================] - 9s 208us/step - loss: -2.6621 - accuracy: 0.1509\n",
      "\n",
      "Epoch 00129: accuracy did not improve from 0.26617\n",
      "Epoch 130/200\n",
      "44299/44299 [==============================] - 9s 208us/step - loss: -2.6754 - accuracy: 0.2735\n",
      "\n",
      "Epoch 00130: accuracy improved from 0.26617 to 0.27348, saving model to weights/weights-lstm.h5\n",
      "Epoch 131/200\n",
      "44299/44299 [==============================] - 9s 209us/step - loss: -2.5107 - accuracy: 0.2506\n",
      "\n",
      "Epoch 00131: accuracy did not improve from 0.27348\n",
      "Epoch 132/200\n",
      "44299/44299 [==============================] - 9s 209us/step - loss: -2.6174 - accuracy: 0.1885\n",
      "\n",
      "Epoch 00132: accuracy did not improve from 0.27348\n",
      "Epoch 133/200\n",
      "44299/44299 [==============================] - 9s 209us/step - loss: -2.6675 - accuracy: 0.1832\n",
      "\n",
      "Epoch 00133: accuracy did not improve from 0.27348\n",
      "Epoch 134/200\n",
      "44299/44299 [==============================] - 9s 207us/step - loss: -2.5516 - accuracy: 0.1607\n",
      "\n",
      "Epoch 00134: accuracy did not improve from 0.27348\n",
      "Epoch 135/200\n",
      "44299/44299 [==============================] - 9s 207us/step - loss: -2.6421 - accuracy: 0.2188\n",
      "\n",
      "Epoch 00135: accuracy did not improve from 0.27348\n",
      "Epoch 136/200\n",
      "44299/44299 [==============================] - 9s 208us/step - loss: -2.6508 - accuracy: 0.3107\n",
      "\n",
      "Epoch 00136: accuracy improved from 0.27348 to 0.31068, saving model to weights/weights-lstm.h5\n",
      "Epoch 137/200\n",
      "44299/44299 [==============================] - 9s 209us/step - loss: -2.6754 - accuracy: 0.3045\n",
      "\n",
      "Epoch 00137: accuracy did not improve from 0.31068\n",
      "Epoch 138/200\n",
      "44299/44299 [==============================] - 9s 208us/step - loss: -2.5849 - accuracy: 0.2120\n",
      "\n",
      "Epoch 00138: accuracy did not improve from 0.31068\n",
      "Epoch 139/200\n",
      "44299/44299 [==============================] - 9s 208us/step - loss: -2.6563 - accuracy: 0.1950\n",
      "\n",
      "Epoch 00139: accuracy did not improve from 0.31068\n",
      "Epoch 140/200\n",
      "44299/44299 [==============================] - 9s 208us/step - loss: -2.6052 - accuracy: 0.2292\n",
      "\n",
      "Epoch 00140: accuracy did not improve from 0.31068\n",
      "Epoch 141/200\n",
      "44299/44299 [==============================] - 9s 207us/step - loss: -2.7048 - accuracy: 0.2333\n",
      "\n",
      "Epoch 00141: accuracy did not improve from 0.31068\n",
      "Epoch 142/200\n",
      "44299/44299 [==============================] - 9s 209us/step - loss: -2.7465 - accuracy: 0.2464\n",
      "\n",
      "Epoch 00142: accuracy did not improve from 0.31068\n",
      "Epoch 143/200\n",
      "44299/44299 [==============================] - 9s 209us/step - loss: -2.6280 - accuracy: 0.1333\n",
      "\n",
      "Epoch 00143: accuracy did not improve from 0.31068\n",
      "Epoch 144/200\n",
      "44299/44299 [==============================] - 9s 209us/step - loss: -2.6376 - accuracy: 0.1626\n",
      "\n",
      "Epoch 00144: accuracy did not improve from 0.31068\n",
      "Epoch 145/200\n",
      "44299/44299 [==============================] - 9s 209us/step - loss: -2.7166 - accuracy: 0.1598\n",
      "\n",
      "Epoch 00145: accuracy did not improve from 0.31068\n",
      "Epoch 146/200\n",
      "44299/44299 [==============================] - 9s 209us/step - loss: -2.6645 - accuracy: 0.3016\n",
      "\n",
      "Epoch 00146: accuracy did not improve from 0.31068\n",
      "Epoch 147/200\n",
      "44299/44299 [==============================] - 12s 274us/step - loss: -2.5739 - accuracy: 0.2405\n",
      "\n",
      "Epoch 00147: accuracy did not improve from 0.31068\n",
      "Epoch 148/200\n",
      "44299/44299 [==============================] - 9s 208us/step - loss: -2.6617 - accuracy: 0.0862\n",
      "\n",
      "Epoch 00148: accuracy did not improve from 0.31068\n",
      "Epoch 149/200\n",
      "44299/44299 [==============================] - 9s 214us/step - loss: -2.6855 - accuracy: 0.1582\n",
      "\n",
      "Epoch 00149: accuracy did not improve from 0.31068\n",
      "Epoch 150/200\n",
      "44299/44299 [==============================] - 9s 210us/step - loss: -2.7380 - accuracy: 0.1842\n",
      "\n",
      "Epoch 00150: accuracy did not improve from 0.31068\n",
      "Epoch 151/200\n",
      "44299/44299 [==============================] - 9s 208us/step - loss: -2.7438 - accuracy: 0.1896\n",
      "\n",
      "Epoch 00151: accuracy did not improve from 0.31068\n",
      "Epoch 152/200\n",
      "44299/44299 [==============================] - 9s 209us/step - loss: -2.6818 - accuracy: 0.1390\n",
      "\n",
      "Epoch 00152: accuracy did not improve from 0.31068\n",
      "Epoch 153/200\n",
      "44299/44299 [==============================] - 9s 210us/step - loss: -2.6942 - accuracy: 0.1965\n",
      "\n",
      "Epoch 00153: accuracy did not improve from 0.31068\n",
      "Epoch 154/200\n",
      "44299/44299 [==============================] - 9s 210us/step - loss: -2.6723 - accuracy: 0.2164\n",
      "\n",
      "Epoch 00154: accuracy did not improve from 0.31068\n",
      "Epoch 155/200\n",
      "44299/44299 [==============================] - 9s 209us/step - loss: -2.6882 - accuracy: 0.2405\n",
      "\n",
      "Epoch 00155: accuracy did not improve from 0.31068\n",
      "Epoch 156/200\n",
      "44299/44299 [==============================] - 9s 210us/step - loss: -2.7315 - accuracy: 0.1684\n",
      "\n",
      "Epoch 00156: accuracy did not improve from 0.31068\n",
      "Epoch 157/200\n",
      "44299/44299 [==============================] - 9s 211us/step - loss: -2.7574 - accuracy: 0.2151\n",
      "\n",
      "Epoch 00157: accuracy did not improve from 0.31068\n",
      "Epoch 158/200\n",
      "44299/44299 [==============================] - 9s 210us/step - loss: -2.7190 - accuracy: 0.2345\n",
      "\n",
      "Epoch 00158: accuracy did not improve from 0.31068\n",
      "Epoch 159/200\n",
      "44299/44299 [==============================] - 9s 211us/step - loss: -2.6910 - accuracy: 0.1282\n",
      "\n",
      "Epoch 00159: accuracy did not improve from 0.31068\n",
      "Epoch 160/200\n",
      "44299/44299 [==============================] - 9s 209us/step - loss: -2.7246 - accuracy: 0.1449\n",
      "\n",
      "Epoch 00160: accuracy did not improve from 0.31068\n",
      "Epoch 161/200\n",
      "44299/44299 [==============================] - 9s 212us/step - loss: -2.5884 - accuracy: 0.1912\n",
      "\n",
      "Epoch 00161: accuracy did not improve from 0.31068\n",
      "Epoch 162/200\n",
      "44299/44299 [==============================] - 9s 209us/step - loss: -2.7897 - accuracy: 0.1546\n",
      "\n",
      "Epoch 00162: accuracy did not improve from 0.31068\n",
      "Epoch 163/200\n",
      "44299/44299 [==============================] - 9s 210us/step - loss: -2.6933 - accuracy: 0.1883\n",
      "\n",
      "Epoch 00163: accuracy did not improve from 0.31068\n",
      "Epoch 164/200\n",
      "44299/44299 [==============================] - 9s 208us/step - loss: -2.6728 - accuracy: 0.1722\n",
      "\n",
      "Epoch 00164: accuracy did not improve from 0.31068\n",
      "Epoch 165/200\n",
      "44299/44299 [==============================] - 9s 211us/step - loss: -2.6825 - accuracy: 0.1247\n",
      "\n",
      "Epoch 00165: accuracy did not improve from 0.31068\n",
      "Epoch 166/200\n",
      "44299/44299 [==============================] - 9s 210us/step - loss: -2.7617 - accuracy: 0.1272\n",
      "\n",
      "Epoch 00166: accuracy did not improve from 0.31068\n",
      "Epoch 167/200\n",
      "44299/44299 [==============================] - 9s 210us/step - loss: -2.7560 - accuracy: 0.1808\n",
      "\n",
      "Epoch 00167: accuracy did not improve from 0.31068\n",
      "Epoch 168/200\n",
      "44299/44299 [==============================] - 9s 209us/step - loss: -2.6390 - accuracy: 0.2216\n",
      "\n",
      "Epoch 00168: accuracy did not improve from 0.31068\n",
      "Epoch 169/200\n",
      "44299/44299 [==============================] - 9s 209us/step - loss: -2.6973 - accuracy: 0.1894\n",
      "\n",
      "Epoch 00169: accuracy did not improve from 0.31068\n",
      "Epoch 170/200\n",
      "44299/44299 [==============================] - 9s 211us/step - loss: -2.7484 - accuracy: 0.1966\n",
      "\n",
      "Epoch 00170: accuracy did not improve from 0.31068\n",
      "Epoch 171/200\n",
      "44299/44299 [==============================] - 9s 210us/step - loss: -2.7415 - accuracy: 0.2018\n",
      "\n",
      "Epoch 00171: accuracy did not improve from 0.31068\n",
      "Epoch 172/200\n",
      "44299/44299 [==============================] - 9s 211us/step - loss: -2.7957 - accuracy: 0.2385\n",
      "\n",
      "Epoch 00172: accuracy did not improve from 0.31068\n",
      "Epoch 173/200\n",
      "44299/44299 [==============================] - 9s 210us/step - loss: -2.7737 - accuracy: 0.2393\n",
      "\n",
      "Epoch 00173: accuracy did not improve from 0.31068\n",
      "Epoch 174/200\n",
      "44299/44299 [==============================] - 9s 211us/step - loss: -2.7726 - accuracy: 0.2408\n",
      "\n",
      "Epoch 00174: accuracy did not improve from 0.31068\n",
      "Epoch 175/200\n",
      "44299/44299 [==============================] - 9s 210us/step - loss: -2.7234 - accuracy: 0.2360\n",
      "\n",
      "Epoch 00175: accuracy did not improve from 0.31068\n",
      "Epoch 176/200\n",
      "44299/44299 [==============================] - 9s 210us/step - loss: -2.7731 - accuracy: 0.1756\n",
      "\n",
      "Epoch 00176: accuracy did not improve from 0.31068\n",
      "Epoch 177/200\n",
      "44299/44299 [==============================] - 9s 210us/step - loss: -2.7324 - accuracy: 0.2385\n",
      "\n",
      "Epoch 00177: accuracy did not improve from 0.31068\n",
      "Epoch 178/200\n",
      "44299/44299 [==============================] - 9s 208us/step - loss: -2.7623 - accuracy: 0.2551\n",
      "\n",
      "Epoch 00178: accuracy did not improve from 0.31068\n",
      "Epoch 179/200\n",
      "44299/44299 [==============================] - 9s 210us/step - loss: -2.6851 - accuracy: 0.1322\n",
      "\n",
      "Epoch 00179: accuracy did not improve from 0.31068\n",
      "Epoch 180/200\n",
      "44299/44299 [==============================] - 9s 211us/step - loss: -2.7412 - accuracy: 0.1980\n",
      "\n",
      "Epoch 00180: accuracy did not improve from 0.31068\n",
      "Epoch 181/200\n",
      "44299/44299 [==============================] - 9s 210us/step - loss: -2.7265 - accuracy: 0.2183\n",
      "\n",
      "Epoch 00181: accuracy did not improve from 0.31068\n",
      "Epoch 182/200\n",
      "44299/44299 [==============================] - 9s 208us/step - loss: -2.8066 - accuracy: 0.1837\n",
      "\n",
      "Epoch 00182: accuracy did not improve from 0.31068\n",
      "Epoch 183/200\n",
      "44299/44299 [==============================] - 9s 211us/step - loss: -2.7665 - accuracy: 0.1868\n",
      "\n",
      "Epoch 00183: accuracy did not improve from 0.31068\n",
      "Epoch 184/200\n",
      "44299/44299 [==============================] - 9s 210us/step - loss: -2.7081 - accuracy: 0.1955\n",
      "\n",
      "Epoch 00184: accuracy did not improve from 0.31068\n",
      "Epoch 185/200\n",
      "44299/44299 [==============================] - 9s 211us/step - loss: -2.6748 - accuracy: 0.3154\n",
      "\n",
      "Epoch 00185: accuracy improved from 0.31068 to 0.31536, saving model to weights/weights-lstm.h5\n",
      "Epoch 186/200\n",
      "44299/44299 [==============================] - 9s 211us/step - loss: -2.8082 - accuracy: 0.2244\n",
      "\n",
      "Epoch 00186: accuracy did not improve from 0.31536\n",
      "Epoch 187/200\n",
      "44299/44299 [==============================] - 9s 210us/step - loss: -2.7932 - accuracy: 0.2268\n",
      "\n",
      "Epoch 00187: accuracy did not improve from 0.31536\n",
      "Epoch 188/200\n",
      "44299/44299 [==============================] - 9s 209us/step - loss: -2.8132 - accuracy: 0.2827\n",
      "\n",
      "Epoch 00188: accuracy did not improve from 0.31536\n",
      "Epoch 189/200\n",
      "44299/44299 [==============================] - 9s 208us/step - loss: -2.7934 - accuracy: 0.2259\n",
      "\n",
      "Epoch 00189: accuracy did not improve from 0.31536\n",
      "Epoch 190/200\n",
      "44299/44299 [==============================] - 9s 209us/step - loss: -2.7731 - accuracy: 0.2271\n",
      "\n",
      "Epoch 00190: accuracy did not improve from 0.31536\n",
      "Epoch 191/200\n",
      "44299/44299 [==============================] - 9s 209us/step - loss: -2.7213 - accuracy: 0.2608\n",
      "\n",
      "Epoch 00191: accuracy did not improve from 0.31536\n",
      "Epoch 192/200\n",
      "44299/44299 [==============================] - 9s 208us/step - loss: -2.7484 - accuracy: 0.1507\n",
      "\n",
      "Epoch 00192: accuracy did not improve from 0.31536\n",
      "Epoch 193/200\n",
      "44299/44299 [==============================] - 9s 208us/step - loss: -2.8184 - accuracy: 0.1915\n",
      "\n",
      "Epoch 00193: accuracy did not improve from 0.31536\n",
      "Epoch 194/200\n",
      "44299/44299 [==============================] - 9s 209us/step - loss: -2.8235 - accuracy: 0.2233\n",
      "\n",
      "Epoch 00194: accuracy did not improve from 0.31536\n",
      "Epoch 195/200\n",
      "44299/44299 [==============================] - 9s 209us/step - loss: -2.7764 - accuracy: 0.1500\n",
      "\n",
      "Epoch 00195: accuracy did not improve from 0.31536\n",
      "Epoch 196/200\n",
      "44299/44299 [==============================] - 9s 209us/step - loss: -2.8037 - accuracy: 0.2086\n",
      "\n",
      "Epoch 00196: accuracy did not improve from 0.31536\n",
      "Epoch 197/200\n",
      "44299/44299 [==============================] - 9s 207us/step - loss: -2.8047 - accuracy: 0.2615\n",
      "\n",
      "Epoch 00197: accuracy did not improve from 0.31536\n",
      "Epoch 198/200\n",
      "44299/44299 [==============================] - 9s 207us/step - loss: -2.8603 - accuracy: 0.2535\n",
      "\n",
      "Epoch 00198: accuracy did not improve from 0.31536\n",
      "Epoch 199/200\n"
     ]
    }
   ],
   "source": [
    "checkpoint_filepath=\"weights/weights-lstm.h5\"\n",
    "checkpoint = ModelCheckpoint(checkpoint_filepath, monitor='accuracy', verbose=1, save_best_only=True, mode='max')\n",
    "\n",
    "# Train the model:\n",
    "lstm_mdn.model.fit(train_X, train_Y, \n",
    "                   epochs=200, \n",
    "                   batch_size=256, \n",
    "                   shuffle=False, \n",
    "                   verbose=1,\n",
    "                   callbacks=[checkpoint, TerminateOnNaN()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "trained_model = lstm_mdn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load a trained model + weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'str' object has no attribute 'decode'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-47-833963546996>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mtrained_model\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel_from_json\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'weights/model-lstm.json'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcustom_objects\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m'MDN'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mMDN\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'LSTM_MDN'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mLSTM_MDN\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----> 2\u001b[0;31m \u001b[0mtrained_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_weights\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'weights/weights-lstm.h5'\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      3\u001b[0m \u001b[0mtrained_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msummary\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      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mn_mixes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/project/hep/demers/mnp3/miniconda3/envs/choreo2020/lib/python3.7/site-packages/keras/engine/saving.py\u001b[0m in \u001b[0;36mload_wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    490\u001b[0m                 \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtmp_filepath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    491\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 492\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mload_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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    493\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    494\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mload_wrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/project/hep/demers/mnp3/miniconda3/envs/choreo2020/lib/python3.7/site-packages/keras/engine/network.py\u001b[0m in \u001b[0;36mload_weights\u001b[0;34m(self, filepath, by_name, skip_mismatch, reshape)\u001b[0m\n\u001b[1;32m   1228\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   1229\u001b[0m                 saving.load_weights_from_hdf5_group(\n\u001b[0;32m-> 1230\u001b[0;31m                     f, self.layers, reshape=reshape)\n\u001b[0m\u001b[1;32m   1231\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'close'\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   1232\u001b[0m                 \u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose\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/project/hep/demers/mnp3/miniconda3/envs/choreo2020/lib/python3.7/site-packages/keras/engine/saving.py\u001b[0m in \u001b[0;36mload_weights_from_hdf5_group\u001b[0;34m(f, layers, reshape)\u001b[0m\n\u001b[1;32m   1199\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mlayer_names\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1200\u001b[0m         \u001b[0mg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1201\u001b[0;31m         \u001b[0mweight_names\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mload_attributes_from_hdf5_group\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'weight_names'\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   1202\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mweight_names\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1203\u001b[0m             \u001b[0mfiltered_layer_names\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/project/hep/demers/mnp3/miniconda3/envs/choreo2020/lib/python3.7/site-packages/keras/engine/saving.py\u001b[0m in \u001b[0;36mload_attributes_from_hdf5_group\u001b[0;34m(group, name)\u001b[0m\n\u001b[1;32m    720\u001b[0m     \"\"\"\n\u001b[1;32m    721\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mgroup\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mattrs\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 722\u001b[0;31m         \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'utf8'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mn\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mgroup\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mattrs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\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[0m\u001b[1;32m    723\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    724\u001b[0m         \u001b[0mdata\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/project/hep/demers/mnp3/miniconda3/envs/choreo2020/lib/python3.7/site-packages/keras/engine/saving.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    720\u001b[0m     \"\"\"\n\u001b[1;32m    721\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mgroup\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mattrs\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 722\u001b[0;31m         \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'utf8'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mn\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mgroup\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mattrs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\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[0m\u001b[1;32m    723\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    724\u001b[0m         \u001b[0mdata\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;31mAttributeError\u001b[0m: 'str' object has no attribute 'decode'"
     ]
    }
   ],
   "source": [
    "trained_model = model_from_json(open('weights/model-lstm.json').read(), custom_objects={'MDN': MDN, 'LSTM_MDN': LSTM_MDN})\n",
    "trained_model.load_weights('weights/weights-lstm.h5')\n",
    "trained_model.summary()\n",
    "\n",
    "n_mixes = 3\n",
    "look_back = 10\n",
    "\n",
    "if use_pca:\n",
    "    means = np.load('pca/rnn_pca_32-means.npy')\n",
    "    components = np.load('pca/rnn_pca_32-components.npy')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### See how well the model can predict the next frame in the input sequence:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Seeding with frame 7103\n"
     ]
    },
    {
     "data": {
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       "\">\n",
       "  Your browser does not support the video tag.\n",
       "</video>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# visualize how well the model learned the input sequence\n",
    "n_frames = 100 # n frames of time slices to generate\n",
    "output_dims = train_X.shape[2]\n",
    "frame = np.random.randint(0,data.full.X.shape[1]-50)\n",
    "print(\"Seeding with frame {}\".format(frame))\n",
    "frames = []\n",
    "\n",
    "test_X = train_X[frame:frame+n_frames] # data to pass into forward prop through the model\n",
    "y_pred = trained_model.model.predict(test_X) # output with shape (n_frames, (output_dims+2) * n_mixes )\n",
    "\n",
    "# partition out the mus, sigs, and mixture weights\n",
    "for i in range(n_frames):\n",
    "    y = y_pred[i].squeeze()\n",
    "    mus = y[:n_mixes*output_dims]\n",
    "    sigs = y[n_mixes*output_dims:n_mixes*output_dims + n_mixes]\n",
    "    alphas = y[-n_mixes:]\n",
    "    # find the most likely distribution - then disregard that number and use the first Gaussian :)\n",
    "    alpha_idx = np.argmax(alphas)\n",
    "    alpha_idx = 0\n",
    "    # pull out the mus that correspond to the selected alpha index\n",
    "    positions = mus[alpha_idx * output_dims:(alpha_idx+1) * output_dims]\n",
    "    frames.append(positions)\n",
    "\n",
    "if use_pca:\n",
    "    # With PCA:\n",
    "    frames = np.dot(frames, components) + means\n",
    "    print(frames.shape)\n",
    "    lstm_predictions = frames.swapaxes(0,1)\n",
    "    lstm_predictions = np.dstack((lstm_predictions[::3,:],lstm_predictions[1::3,:],lstm_predictions[2::3,:]))\n",
    "else:\n",
    "    # No PCA:\n",
    "    frames = np.array(frames)\n",
    "    lstm_predictions = np.dstack((frames.T[::3,:],frames.T[1::3,:],frames.T[2::3,:]))\n",
    "    \n",
    "HTML(animate_ghost(data.selected.X[:,frame:,:], lstm_predictions[:,:,:], frames=n_frames, edges=data.selected.edges, colors='blue', ghost_shift = 0.3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now generate new sequences!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * seeding with 16898\n"
     ]
    },
    {
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       "\">\n",
       "  Your browser does not support the video tag.\n",
       "</video>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_frames = 128 # n frames of time slices to generate\n",
    "frames = []\n",
    "seed = np.random.randint(0, len(train_X)-1)\n",
    "x = np.expand_dims(train_X[seed], axis=0)\n",
    "print(' * seeding with', seed)\n",
    "\n",
    "for i in range(n_frames):\n",
    "    y = trained_model.model.predict(x).squeeze()\n",
    "    mus = y[:n_mixes*output_dims]\n",
    "    sigs = y[n_mixes*output_dims:-n_mixes]\n",
    "    alphas = softmax(y[-n_mixes:])\n",
    "\n",
    "    # select the alpha channel to use\n",
    "    alpha_idx = np.argmax(alphas)\n",
    "\n",
    "    # grab the mus and sigs associated with the selected alpha_idx\n",
    "    frame_mus = mus.ravel()[alpha_idx*output_dims : (alpha_idx+1)*output_dims]\n",
    "    frame_sig = sigs[alpha_idx] / 100\n",
    "\n",
    "    # now sample from each Gaussian\n",
    "    positions = [np.random.normal(loc=m, scale=frame_sig) for m in frame_mus]\n",
    "    positions = frame_mus\n",
    "\n",
    "    # add these positions to the results\n",
    "    frames.append(positions)\n",
    "\n",
    "    # pull out a new training example - stack the new result on\n",
    "    # all values after the first from the bottom-most value in the x's\n",
    "    start = x[:,1:,:]\n",
    "    end = np.expand_dims( np.expand_dims(positions, axis=0), axis=0 )\n",
    "    x = np.concatenate((start, end), axis=1)\n",
    "    \n",
    "frames = np.array(frames)\n",
    "\n",
    "if use_pca:\n",
    "    # With PCA:\n",
    "    frames = np.dot(frames, components) + means\n",
    "#     print(frames.shape)\n",
    "    lstm_predictions = frames.swapaxes(0,1)\n",
    "    lstm_predictions = np.dstack((lstm_predictions[::3,:],lstm_predictions[1::3,:],lstm_predictions[2::3,:]))\n",
    "else:\n",
    "    # No PCA:\n",
    "    lstm_predictions = np.dstack((frames.T[::3,:],frames.T[1::3,:],frames.T[2::3,:]))\n",
    "    \n",
    "prompt_plus_generated_seq = np.concatenate((data.selected.X[:,seed:seed+look_back,:],lstm_predictions), axis=1)\n",
    "HTML(animate_ghost(data.selected.X[:,seed:seed+look_back+n_frames:,:], prompt_plus_generated_seq, frames=look_back+n_frames, edges=data.selected.edges, colors='blue'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
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