Hydra Autoencoder.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Autoencoding Graphical User Interfaces\n",
    "\n",
    "In our initial approach to solving code2pix, we assumed we needed to pretrain a convolutional model that could decode textual data into images of graphical user interfaces. The purpose of training the following autoencoder is to train the decoder that we then use in the code2pix model. However, last minute experiments showed that training the autoencoder is **not strictly necessary** to train code2pix. If you would like to skip straight to code2pix, feel free to initialize the model, skip training, and just save the model for its architecture. Code2Pix will just needs the layer architecture constructed in this notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ngundotra/.conda/envs/torchenv/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n",
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt \n",
    "% matplotlib inline\n",
    "import os\n",
    "from os.path import join\n",
    "\n",
    "import keras\n",
    "from keras.models import Sequential\n",
    "from keras.layers import (LSTM, Dense, Conv2D, Input, Reshape, concatenate, MaxPooling2D,Dropout, Flatten, \n",
    "RepeatVector, UpSampling2D, Conv1D, Permute, BatchNormalization, Activation, UpSampling2D)\n",
    "from keras import Model\n",
    "from keras.engine.topology import Layer, InputSpec\n",
    "from keras import backend as K\n",
    "from keras.optimizers import RMSprop\n",
    "from keras.utils import to_categorical\n",
    "from keras.losses import mean_squared_error\n",
    "from keras.preprocessing import sequence"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hydra Autoencoder\n",
    "\n",
    "Model that understands our synthetic datasets (android, ios, web) interfaces, using a shared CNN to encode each picture, and unique heads to decode for each platform (3 heads). It takes about 15 minutes to train all three heads and the encoder on two Nvidia 1080 Ti-s."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 1\n",
      "Train on 1200 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "1200/1200 [==============================] - 13s 11ms/step - loss: 0.4835 - acc: 0.0046 - val_loss: 0.3131 - val_acc: 0.0046\n",
      "300/300 [==============================] - 1s 4ms/step\n",
      "Train on 1200 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "1200/1200 [==============================] - 11s 9ms/step - loss: 0.5459 - acc: 0.4587 - val_loss: 0.6028 - val_acc: 0.3122\n",
      "300/300 [==============================] - 1s 3ms/step\n",
      "Train on 1200 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "1200/1200 [==============================] - 11s 10ms/step - loss: 0.5404 - acc: 0.3138 - val_loss: 0.3864 - val_acc: 0.3527\n",
      "300/300 [==============================] - 1s 3ms/step\n",
      "Model had avg val loss of: 0.43409594668282403\n",
      "Best avg validation loss is 0.43409594668282403\n",
      "Epoch: 2\n",
      "Train on 1200 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "1200/1200 [==============================] - 11s 9ms/step - loss: 0.2470 - acc: 0.0052 - val_loss: 0.2188 - val_acc: 0.0057\n",
      "300/300 [==============================] - 1s 3ms/step\n",
      "Train on 1200 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "1200/1200 [==============================] - 11s 9ms/step - loss: 0.2138 - acc: 0.5194 - val_loss: 0.2263 - val_acc: 0.5017\n",
      "300/300 [==============================] - 1s 3ms/step\n",
      "Train on 1200 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "1200/1200 [==============================] - 11s 9ms/step - loss: 0.2127 - acc: 0.3643 - val_loss: 0.2422 - val_acc: 0.3818\n",
      "300/300 [==============================] - 1s 3ms/step\n",
      "Model had avg val loss of: 0.22908878293302326\n",
      "Best avg validation loss is 0.43409594668282403\n",
      "Epoch: 3\n",
      "Train on 1200 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "1200/1200 [==============================] - 11s 9ms/step - loss: 0.2051 - acc: 0.0054 - val_loss: 0.2715 - val_acc: 0.0055\n",
      "300/300 [==============================] - 1s 3ms/step\n",
      "Train on 1200 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "1200/1200 [==============================] - 11s 9ms/step - loss: 0.1650 - acc: 0.5195 - val_loss: 0.6579 - val_acc: 0.5018\n",
      "300/300 [==============================] - 1s 3ms/step\n",
      "Train on 1200 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "1200/1200 [==============================] - 11s 9ms/step - loss: 0.1603 - acc: 0.3649 - val_loss: 0.1679 - val_acc: 0.3788\n",
      "300/300 [==============================] - 1s 3ms/step\n",
      "Model had avg val loss of: 0.36575216551621753\n",
      "Best avg validation loss is 0.43409594668282403\n",
      "Epoch: 4\n",
      "Train on 1200 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "1200/1200 [==============================] - 11s 9ms/step - loss: 0.1978 - acc: 0.0060 - val_loss: 0.2098 - val_acc: 0.0048\n",
      "300/300 [==============================] - 1s 3ms/step\n",
      "Train on 1200 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "1200/1200 [==============================] - 11s 9ms/step - loss: 0.1586 - acc: 0.5195 - val_loss: 0.2254 - val_acc: 0.5012\n",
      "300/300 [==============================] - 1s 3ms/step\n",
      "Train on 1200 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "1200/1200 [==============================] - 11s 9ms/step - loss: 0.1562 - acc: 0.3649 - val_loss: 0.1543 - val_acc: 0.3793\n",
      "300/300 [==============================] - 1s 3ms/step\n",
      "Model had avg val loss of: 0.19651010354359943\n",
      "Best avg validation loss is 0.43409594668282403\n",
      "Epoch: 5\n",
      "Train on 1200 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "1200/1200 [==============================] - 11s 9ms/step - loss: 0.1932 - acc: 0.0063 - val_loss: 0.1945 - val_acc: 0.0063\n",
      "300/300 [==============================] - 1s 3ms/step\n",
      "Train on 1200 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "1200/1200 [==============================] - 11s 9ms/step - loss: 0.1545 - acc: 0.5195 - val_loss: 0.2026 - val_acc: 0.5013\n",
      "300/300 [==============================] - 1s 3ms/step\n",
      "Train on 1200 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "1200/1200 [==============================] - 11s 9ms/step - loss: 0.1527 - acc: 0.3650 - val_loss: 0.2117 - val_acc: 0.3818\n",
      "300/300 [==============================] - 1s 3ms/step\n",
      "Model had avg val loss of: 0.20296844952636295\n",
      "Best avg validation loss is 0.43409594668282403\n",
      "Epoch: 6\n",
      "Train on 1200 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "1200/1200 [==============================] - 11s 9ms/step - loss: 0.1908 - acc: 0.0064 - val_loss: 0.1912 - val_acc: 0.0058\n",
      "300/300 [==============================] - 1s 3ms/step\n",
      "Train on 1200 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "1200/1200 [==============================] - 11s 9ms/step - loss: 0.1526 - acc: 0.5195 - val_loss: 0.1866 - val_acc: 0.5017\n",
      "300/300 [==============================] - 1s 3ms/step\n",
      "Train on 1200 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "1200/1200 [==============================] - 11s 9ms/step - loss: 0.1504 - acc: 0.3651 - val_loss: 0.2096 - val_acc: 0.3818\n",
      "300/300 [==============================] - 1s 3ms/step\n",
      "Model had avg val loss of: 0.19579220493634542\n",
      "Best avg validation loss is 0.43409594668282403\n",
      "Epoch: 7\n",
      "Train on 1200 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "1200/1200 [==============================] - 11s 9ms/step - loss: 0.1896 - acc: 0.0065 - val_loss: 0.1895 - val_acc: 0.0065\n",
      "300/300 [==============================] - 1s 3ms/step\n",
      "Train on 1200 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "1200/1200 [==============================] - 11s 9ms/step - loss: 0.1508 - acc: 0.5195 - val_loss: 0.1769 - val_acc: 0.5017\n",
      "300/300 [==============================] - 1s 3ms/step\n",
      "Train on 1200 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "1200/1200 [==============================] - 11s 9ms/step - loss: 0.1486 - acc: 0.3652 - val_loss: 0.2134 - val_acc: 0.3818\n",
      "300/300 [==============================] - 1s 3ms/step\n",
      "Model had avg val loss of: 0.19328342676162721\n",
      "Best avg validation loss is 0.43409594668282403\n",
      "Epoch: 8\n",
      "Train on 1200 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "1200/1200 [==============================] - 11s 9ms/step - loss: 0.1886 - acc: 0.0066 - val_loss: 0.1924 - val_acc: 0.0061\n",
      "300/300 [==============================] - 1s 3ms/step\n",
      "Train on 1200 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "1200/1200 [==============================] - 11s 9ms/step - loss: 0.1497 - acc: 0.5195 - val_loss: 0.1609 - val_acc: 0.5018\n",
      "300/300 [==============================] - 1s 3ms/step\n",
      "Train on 1200 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "1200/1200 [==============================] - 11s 9ms/step - loss: 0.1468 - acc: 0.3652 - val_loss: 0.1897 - val_acc: 0.3818\n",
      "300/300 [==============================] - 1s 3ms/step\n",
      "Model had avg val loss of: 0.18097938040892284\n",
      "Best avg validation loss is 0.43409594668282403\n",
      "Epoch: 9\n",
      "Train on 1200 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "1200/1200 [==============================] - 11s 9ms/step - loss: 0.1880 - acc: 0.0067 - val_loss: 0.1889 - val_acc: 0.0066\n",
      "300/300 [==============================] - 1s 3ms/step\n",
      "Train on 1200 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "1200/1200 [==============================] - 11s 9ms/step - loss: 0.1487 - acc: 0.5195 - val_loss: 0.2267 - val_acc: 0.5012\n",
      "300/300 [==============================] - 1s 3ms/step\n",
      "Train on 1200 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "1200/1200 [==============================] - 11s 9ms/step - loss: 0.1456 - acc: 0.3653 - val_loss: 0.1457 - val_acc: 0.3810\n",
      "300/300 [==============================] - 1s 3ms/step\n",
      "Model had avg val loss of: 0.1870827837785085\n",
      "Best avg validation loss is 0.43409594668282403\n",
      "Epoch: 10\n",
      "Train on 1200 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "1200/1200 [==============================] - 11s 9ms/step - loss: 0.1872 - acc: 0.0067 - val_loss: 0.1890 - val_acc: 0.0065\n",
      "300/300 [==============================] - 1s 3ms/step\n",
      "Train on 1200 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "1200/1200 [==============================] - 11s 9ms/step - loss: 0.1480 - acc: 0.5196 - val_loss: 0.1767 - val_acc: 0.5015\n",
      "300/300 [==============================] - 1s 3ms/step\n",
      "Train on 1200 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "1200/1200 [==============================] - 11s 9ms/step - loss: 0.1446 - acc: 0.3653 - val_loss: 0.1472 - val_acc: 0.3815\n",
      "300/300 [==============================] - 1s 3ms/step\n",
      "Model had avg val loss of: 0.17098331650098164\n",
      "Best avg validation loss is 0.43409594668282403\n",
      "Epoch: 11\n",
      "Train on 1200 samples, validate on 300 samples\n",
      "Epoch 1/1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1200/1200 [==============================] - 11s 9ms/step - loss: 0.1867 - acc: 0.0068 - val_loss: 0.1879 - val_acc: 0.0069\n",
      "300/300 [==============================] - 1s 3ms/step\n",
      "Train on 1200 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "1200/1200 [==============================] - 11s 9ms/step - loss: 0.1471 - acc: 0.5196 - val_loss: 0.1584 - val_acc: 0.5018\n",
      "300/300 [==============================] - 1s 3ms/step\n",
      "Train on 1200 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "1200/1200 [==============================] - 11s 9ms/step - loss: 0.1441 - acc: 0.3654 - val_loss: 0.4554 - val_acc: 0.3818\n",
      "300/300 [==============================] - 1s 3ms/step\n",
      "Model had avg val loss of: 0.2672271705998315\n",
      "Best avg validation loss is 0.43409594668282403\n",
      "Epoch: 12\n",
      "Train on 1200 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "1200/1200 [==============================] - 11s 9ms/step - loss: 0.1864 - acc: 0.0068 - val_loss: 0.1864 - val_acc: 0.0069\n",
      "300/300 [==============================] - 1s 3ms/step\n",
      "Train on 1200 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "1200/1200 [==============================] - 11s 9ms/step - loss: 0.1467 - acc: 0.5196 - val_loss: 0.1566 - val_acc: 0.5018\n",
      "300/300 [==============================] - 1s 3ms/step\n",
      "Train on 1200 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "1200/1200 [==============================] - 11s 9ms/step - loss: 0.1442 - acc: 0.3654 - val_loss: 0.2594 - val_acc: 0.3818\n",
      "300/300 [==============================] - 1s 3ms/step\n",
      "Model had avg val loss of: 0.20082141041755674\n",
      "Best avg validation loss is 0.43409594668282403\n",
      "Epoch: 13\n",
      "Train on 1200 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "1200/1200 [==============================] - 11s 9ms/step - loss: 0.1861 - acc: 0.0068 - val_loss: 0.1882 - val_acc: 0.0062\n",
      "300/300 [==============================] - 1s 3ms/step\n",
      "Train on 1200 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "1200/1200 [==============================] - 11s 9ms/step - loss: 0.1462 - acc: 0.5196 - val_loss: 0.1613 - val_acc: 0.5018\n",
      "300/300 [==============================] - 1s 3ms/step\n",
      "Train on 1200 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "1200/1200 [==============================] - 11s 9ms/step - loss: 0.1426 - acc: 0.3654 - val_loss: 0.1401 - val_acc: 0.3810\n",
      "300/300 [==============================] - 1s 3ms/step\n",
      "Model had avg val loss of: 0.16323615067534977\n",
      "Best avg validation loss is 0.43409594668282403\n",
      "Epoch: 14\n",
      "Train on 1200 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "1200/1200 [==============================] - 11s 9ms/step - loss: 0.1857 - acc: 0.0068 - val_loss: 0.1856 - val_acc: 0.0068\n",
      "300/300 [==============================] - 1s 3ms/step\n",
      "Train on 1200 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "1200/1200 [==============================] - 11s 9ms/step - loss: 0.1462 - acc: 0.5196 - val_loss: 0.1519 - val_acc: 0.5018\n",
      "300/300 [==============================] - 1s 3ms/step\n",
      "Train on 1200 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "1200/1200 [==============================] - 11s 9ms/step - loss: 0.1419 - acc: 0.3655 - val_loss: 0.1375 - val_acc: 0.3815\n",
      "300/300 [==============================] - 1s 3ms/step\n",
      "Model had avg val loss of: 0.1583106345600552\n",
      "Best avg validation loss is 0.43409594668282403\n",
      "Epoch: 15\n",
      "Train on 1200 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "1200/1200 [==============================] - 11s 9ms/step - loss: 0.1853 - acc: 0.0069 - val_loss: 0.1871 - val_acc: 0.0070\n",
      "300/300 [==============================] - 1s 3ms/step\n",
      "Train on 1200 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "1200/1200 [==============================] - 11s 9ms/step - loss: 0.1452 - acc: 0.5196 - val_loss: 0.1520 - val_acc: 0.5018\n",
      "300/300 [==============================] - 1s 3ms/step\n",
      "Train on 1200 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "1200/1200 [==============================] - 11s 9ms/step - loss: 0.1414 - acc: 0.3655 - val_loss: 0.1371 - val_acc: 0.3809\n",
      "300/300 [==============================] - 1s 3ms/step\n",
      "Model had avg val loss of: 0.15869325379530588\n",
      "Best avg validation loss is 0.43409594668282403\n",
      "Epoch: 16\n",
      "Train on 1200 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "1200/1200 [==============================] - 11s 9ms/step - loss: 0.1851 - acc: 0.0069 - val_loss: 0.1854 - val_acc: 0.0069\n",
      "300/300 [==============================] - 1s 3ms/step\n",
      "Train on 1200 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "1200/1200 [==============================] - 11s 9ms/step - loss: 0.1452 - acc: 0.5196 - val_loss: 0.1526 - val_acc: 0.5018\n",
      "300/300 [==============================] - 1s 3ms/step\n",
      "Train on 1200 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "1200/1200 [==============================] - 11s 9ms/step - loss: 0.1410 - acc: 0.3655 - val_loss: 0.1695 - val_acc: 0.3818\n",
      "300/300 [==============================] - 1s 3ms/step\n",
      "Model had avg val loss of: 0.16915018637975057\n",
      "Best avg validation loss is 0.43409594668282403\n",
      "Epoch: 17\n",
      "Train on 1200 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "1200/1200 [==============================] - 11s 9ms/step - loss: 0.1849 - acc: 0.0069 - val_loss: 0.1860 - val_acc: 0.0069\n",
      "300/300 [==============================] - 1s 3ms/step\n",
      "Train on 1200 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "1200/1200 [==============================] - 11s 9ms/step - loss: 0.1447 - acc: 0.5196 - val_loss: 0.1510 - val_acc: 0.5018\n",
      "300/300 [==============================] - 1s 3ms/step\n",
      "Train on 1200 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "1200/1200 [==============================] - 11s 9ms/step - loss: 0.1404 - acc: 0.3656 - val_loss: 0.1365 - val_acc: 0.3807\n",
      "300/300 [==============================] - 1s 3ms/step\n",
      "Model had avg val loss of: 0.15781751480367448\n",
      "Best avg validation loss is 0.43409594668282403\n",
      "Epoch: 18\n",
      "Train on 1200 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "1200/1200 [==============================] - 11s 9ms/step - loss: 0.1846 - acc: 0.0069 - val_loss: 0.1842 - val_acc: 0.0070\n",
      "300/300 [==============================] - 1s 3ms/step\n",
      "Train on 1200 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "1200/1200 [==============================] - 11s 9ms/step - loss: 0.1445 - acc: 0.5196 - val_loss: 0.1505 - val_acc: 0.5018\n",
      "300/300 [==============================] - 1s 3ms/step\n",
      "Train on 1200 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "1200/1200 [==============================] - 11s 9ms/step - loss: 0.1401 - acc: 0.3656 - val_loss: 0.1357 - val_acc: 0.3812\n",
      "300/300 [==============================] - 1s 3ms/step\n",
      "Model had avg val loss of: 0.15678167786863118\n",
      "Best avg validation loss is 0.43409594668282403\n",
      "Epoch: 19\n",
      "Train on 1200 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "1200/1200 [==============================] - 11s 9ms/step - loss: 0.1845 - acc: 0.0069 - val_loss: 0.1856 - val_acc: 0.0071\n",
      "300/300 [==============================] - 1s 3ms/step\n",
      "Train on 1200 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "1200/1200 [==============================] - 11s 9ms/step - loss: 0.1443 - acc: 0.5196 - val_loss: 0.1494 - val_acc: 0.5018\n",
      "300/300 [==============================] - 1s 3ms/step\n",
      "Train on 1200 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "1200/1200 [==============================] - 11s 9ms/step - loss: 0.1398 - acc: 0.3656 - val_loss: 0.1360 - val_acc: 0.3804\n",
      "300/300 [==============================] - 1s 3ms/step\n",
      "Model had avg val loss of: 0.15701211445861393\n",
      "Best avg validation loss is 0.43409594668282403\n",
      "Epoch: 20\n",
      "Train on 1200 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "1200/1200 [==============================] - 11s 9ms/step - loss: 0.1843 - acc: 0.0069 - val_loss: 0.1850 - val_acc: 0.0070\n",
      "300/300 [==============================] - 1s 3ms/step\n",
      "Train on 1200 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "1200/1200 [==============================] - 11s 9ms/step - loss: 0.1441 - acc: 0.5196 - val_loss: 0.1496 - val_acc: 0.5019\n",
      "300/300 [==============================] - 1s 3ms/step\n",
      "Train on 1200 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "1200/1200 [==============================] - 11s 9ms/step - loss: 0.1394 - acc: 0.3656 - val_loss: 0.1352 - val_acc: 0.3815\n",
      "300/300 [==============================] - 1s 3ms/step\n",
      "Model had avg val loss of: 0.15658335076438057\n",
      "Best avg validation loss is 0.43409594668282403\n",
      "Epoch: 21\n",
      "Train on 1200 samples, validate on 300 samples\n",
      "Epoch 1/1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1200/1200 [==============================] - 11s 9ms/step - loss: 0.1842 - acc: 0.0069 - val_loss: 0.1859 - val_acc: 0.0071\n",
      "300/300 [==============================] - 1s 3ms/step\n",
      "Train on 1200 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "1200/1200 [==============================] - 11s 9ms/step - loss: 0.1439 - acc: 0.5196 - val_loss: 0.1499 - val_acc: 0.5018\n",
      "300/300 [==============================] - 1s 3ms/step\n",
      "Train on 1200 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "1200/1200 [==============================] - 11s 9ms/step - loss: 0.1392 - acc: 0.3657 - val_loss: 0.1400 - val_acc: 0.3806\n",
      "300/300 [==============================] - 1s 3ms/step\n",
      "Model had avg val loss of: 0.15858489149146607\n",
      "Best avg validation loss is 0.43409594668282403\n",
      "Epoch: 22\n",
      "Train on 1200 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "1200/1200 [==============================] - 11s 9ms/step - loss: 0.1840 - acc: 0.0069 - val_loss: 0.1854 - val_acc: 0.0070\n",
      "300/300 [==============================] - 1s 3ms/step\n",
      "Train on 1200 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "1200/1200 [==============================] - 11s 9ms/step - loss: 0.1437 - acc: 0.5196 - val_loss: 0.1492 - val_acc: 0.5019\n",
      "300/300 [==============================] - 1s 3ms/step\n",
      "Train on 1200 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "1200/1200 [==============================] - 11s 9ms/step - loss: 0.1390 - acc: 0.3657 - val_loss: 0.1351 - val_acc: 0.3816\n",
      "300/300 [==============================] - 1s 3ms/step\n",
      "Model had avg val loss of: 0.1565568486849467\n",
      "Best avg validation loss is 0.43409594668282403\n",
      "Epoch: 23\n",
      "Train on 1200 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "1200/1200 [==============================] - 11s 9ms/step - loss: 0.1839 - acc: 0.0070 - val_loss: 0.1854 - val_acc: 0.0071\n",
      "300/300 [==============================] - 1s 3ms/step\n",
      "Train on 1200 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "1200/1200 [==============================] - 11s 9ms/step - loss: 0.1436 - acc: 0.5196 - val_loss: 0.1484 - val_acc: 0.5020\n",
      "300/300 [==============================] - 1s 3ms/step\n",
      "Train on 1200 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "1200/1200 [==============================] - 11s 9ms/step - loss: 0.1387 - acc: 0.3657 - val_loss: 0.1376 - val_acc: 0.3812\n",
      "300/300 [==============================] - 1s 3ms/step\n",
      "Model had avg val loss of: 0.15717394212881722\n",
      "Best avg validation loss is 0.43409594668282403\n",
      "Epoch: 24\n",
      "Train on 1200 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "1200/1200 [==============================] - 11s 9ms/step - loss: 0.1837 - acc: 0.0070 - val_loss: 0.1877 - val_acc: 0.0069\n",
      "300/300 [==============================] - 1s 3ms/step\n",
      "Train on 1200 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "1200/1200 [==============================] - 11s 9ms/step - loss: 0.1435 - acc: 0.5196 - val_loss: 0.1482 - val_acc: 0.5018\n",
      "300/300 [==============================] - 1s 3ms/step\n",
      "Train on 1200 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "1200/1200 [==============================] - 11s 9ms/step - loss: 0.1384 - acc: 0.3657 - val_loss: 0.1344 - val_acc: 0.3810\n",
      "300/300 [==============================] - 1s 3ms/step\n",
      "Model had avg val loss of: 0.15679121070437962\n",
      "Best avg validation loss is 0.43409594668282403\n",
      "Epoch: 25\n",
      "Train on 1200 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "1200/1200 [==============================] - 11s 9ms/step - loss: 0.1837 - acc: 0.0070 - val_loss: 0.1850 - val_acc: 0.0071\n",
      "300/300 [==============================] - 1s 3ms/step\n",
      "Train on 1200 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "1200/1200 [==============================] - 11s 9ms/step - loss: 0.1432 - acc: 0.5196 - val_loss: 0.1482 - val_acc: 0.5019\n",
      "300/300 [==============================] - 1s 3ms/step\n",
      "Train on 1200 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "1200/1200 [==============================] - 11s 9ms/step - loss: 0.1383 - acc: 0.3658 - val_loss: 0.1343 - val_acc: 0.3813\n",
      "300/300 [==============================] - 1s 3ms/step\n",
      "Model had avg val loss of: 0.1558398103713989\n",
      "Best avg validation loss is 0.43409594668282403\n",
      "Epoch: 26\n",
      "Train on 1200 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "1200/1200 [==============================] - 11s 9ms/step - loss: 0.1836 - acc: 0.0070 - val_loss: 0.1855 - val_acc: 0.0070\n",
      "300/300 [==============================] - 1s 3ms/step\n",
      "Train on 1200 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "1200/1200 [==============================] - 11s 9ms/step - loss: 0.1431 - acc: 0.5196 - val_loss: 0.1478 - val_acc: 0.5018\n",
      "300/300 [==============================] - 1s 3ms/step\n",
      "Train on 1200 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "1200/1200 [==============================] - 11s 9ms/step - loss: 0.1380 - acc: 0.3658 - val_loss: 0.1344 - val_acc: 0.3814\n",
      "300/300 [==============================] - 1s 3ms/step\n",
      "Model had avg val loss of: 0.15590417948034074\n",
      "Best avg validation loss is 0.43409594668282403\n",
      "Epoch: 27\n",
      "Train on 1200 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "1200/1200 [==============================] - 11s 9ms/step - loss: 0.1834 - acc: 0.0070 - val_loss: 0.1862 - val_acc: 0.0071\n",
      "300/300 [==============================] - 1s 3ms/step\n",
      "Train on 1200 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "1200/1200 [==============================] - 11s 9ms/step - loss: 0.1430 - acc: 0.5196 - val_loss: 0.1504 - val_acc: 0.5018\n",
      "300/300 [==============================] - 1s 3ms/step\n",
      "Train on 1200 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "1200/1200 [==============================] - 11s 9ms/step - loss: 0.1379 - acc: 0.3658 - val_loss: 0.1337 - val_acc: 0.3814\n",
      "300/300 [==============================] - 1s 3ms/step\n",
      "Model had avg val loss of: 0.15675139950381384\n",
      "Best avg validation loss is 0.43409594668282403\n",
      "Epoch: 28\n",
      "Train on 1200 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "1200/1200 [==============================] - 11s 9ms/step - loss: 0.1834 - acc: 0.0070 - val_loss: 0.1843 - val_acc: 0.0070\n",
      "300/300 [==============================] - 1s 3ms/step\n",
      "Train on 1200 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "1200/1200 [==============================] - 11s 9ms/step - loss: 0.1430 - acc: 0.5196 - val_loss: 0.1477 - val_acc: 0.5019\n",
      "300/300 [==============================] - 1s 3ms/step\n",
      "Train on 1200 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "1200/1200 [==============================] - 11s 9ms/step - loss: 0.1377 - acc: 0.3658 - val_loss: 0.1344 - val_acc: 0.3814\n",
      "300/300 [==============================] - 1s 3ms/step\n",
      "Model had avg val loss of: 0.15547384520371757\n",
      "Best avg validation loss is 0.43409594668282403\n"
     ]
    }
   ],
   "source": [
    "def load_guis(data_dir):\n",
    "    \"\"\"Loads in target data (picture data) in a given directory.\n",
    "    Returns a np.array of the data loaded.\"\"\"\n",
    "    GUIS = []\n",
    "    training_GUI_listing = os.listdir(data_dir)\n",
    "    for listing in training_GUI_listing:\n",
    "        if listing.endswith('.npz'):\n",
    "            temp = np.load(join(data_dir, listing))['features']\n",
    "            # reshape to be flat\n",
    "            GUIS += [temp]\n",
    "    GUIS = np.array(GUIS)\n",
    "    return GUIS\n",
    "\n",
    "def make_head():\n",
    "    \"\"\"Returns a standard decoder model\"\"\"\n",
    "    input_layer = Input(shape=(16, 16, 16))\n",
    "    last = Conv2D(128, (4, 4), padding='same', activation='relu')(input_layer)\n",
    "\n",
    "    last = BatchNormalization()(last)\n",
    "    last = UpSampling2D(size=(2, 2))(last)\n",
    "    last = Conv2D(96, (3, 3), padding='same', activation='relu')(last)\n",
    "    # model.add(Conv2DTranspose(96, (2, 2), strides=(2, 2)))\n",
    "    last = BatchNormalization()(last)\n",
    "    last = UpSampling2D(size=(4, 4))(last)\n",
    "    last = Conv2D(32, (3, 3), padding='same', activation='relu')(last)\n",
    "    # model.add(Conv2DTranspose(32, (2, 2), strides=(2, 2)))\n",
    "    last = BatchNormalization()(last)\n",
    "    last = UpSampling2D(size=(2, 2))(last)\n",
    "    # model.add(Conv2D(16, (3, 3), padding='same', activation='relu'))\n",
    "    last = Conv2D(3, (3, 3), padding='same', activation='sigmoid')(last)\n",
    "    # model.add(Conv2DTranspose(color, (3, 3), strides=(2, 2)))\n",
    "    model = Model(input_layer, last)\n",
    "    return model\n",
    "    \n",
    "def make_hydra(loss='binary_crossentropy', optimizer='rmsprop'):\n",
    "    \"\"\"Creates 3 headed autoencoder model to handle the different datasets.\n",
    "    Returns [android, ios, web] models\"\"\"\n",
    "    keras.backend.clear_session()\n",
    "\n",
    "    input_layer = Input(shape=(256, 256, 3))\n",
    "    drop_rate = 0.25\n",
    "    last = Conv2D(64, (5, 5), strides=(2,2), activation='relu')(input_layer)\n",
    "    last = Conv2D(128, (4, 4), padding='same', strides=(1,1), activation='relu')(last)\n",
    "    last = MaxPooling2D(pool_size=(2,2))(last)\n",
    "    last = Dropout(drop_rate)(last)\n",
    "    last = Conv2D(128, (4, 4), padding='same', strides=(2,2), activation='relu')(last)\n",
    "    last = Conv2D(256, (3, 3), padding='same', strides=(1,1), activation='relu')(last)\n",
    "    last = Conv2D(128, (3, 3), padding='same', strides=(1,1), activation='relu')(last)\n",
    "    last = MaxPooling2D(pool_size=(2, 2))(last)\n",
    "    last = Dropout(drop_rate)(last)\n",
    "\n",
    "    last = Conv2D(128, (3, 3), padding='same', strides=(1,1), activation='relu')(last)\n",
    "    last = Conv2D(64, (3, 3), padding='same', strides=(1,1), activation='relu')(last)\n",
    "    last = Conv2D(16, (3, 3), padding='same', strides=(1,1), activation='relu')(last)\n",
    "\n",
    "    encoder = Model(input_layer, last)\n",
    "    android = make_head()\n",
    "    ios = make_head()\n",
    "    web = make_head()\n",
    "    \n",
    "    models = []\n",
    "    for model in [android, ios, web]:\n",
    "        head_spout = model(encoder.output)\n",
    "        head = Model(input_layer, head_spout)\n",
    "        head.compile(optimizer=optimizer, loss=loss, metrics=['accuracy'])\n",
    "        models.append(head)\n",
    "    return models\n",
    "\n",
    "def train_hydra(epochs, models, datasets, val_split=0.2):\n",
    "    \"\"\"Assumes models=[android, ios, web], and datasets ordered alphabetically as well.\n",
    "    Trains the models sequentially, 1 epoch after the other. Validation split is last\n",
    "    val_split % of the data.\n",
    "    \n",
    "    ~16 epochs seems to give good perf, >30 pretty good\"\"\"\n",
    "    train = [data[:int((1 - val_split)*len(data))] for data in datasets]\n",
    "    validation = [data[int((1-val_split)*len(data)):] for data in datasets]\n",
    "    val_loss = 20\n",
    "    val_best = 20\n",
    "    for i in range(epochs):\n",
    "        print(\"Epoch: {}\".format(i+1))\n",
    "        val_loss = 0\n",
    "        for model, data, val in zip(models, train, validation):\n",
    "            model.fit(data, data, batch_size=16, validation_data=(val, val))\n",
    "            val_loss += model.evaluate(val, val)[0]\n",
    "        print(\"Model had avg val loss of: {}\".format(val_loss / 3))\n",
    "        if val_loss < val_best:\n",
    "            val_best = val_loss / 3\n",
    "        print(\"Best avg validation loss is {}\".format(val_best))\n",
    "            \n",
    "android_data = load_guis('android/training_features/')\n",
    "ios_data = load_guis('ios/training_features/')\n",
    "web_data = load_guis('web/training_features/')\n",
    "models = make_hydra()\n",
    "train_hydra(28, models, [android_data, ios_data, web_data])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualizing the models' performance. Example visualizations are saved in `android_hydra.png`, `ios_hydra.png`, and `web_hydra.png` respectively. To change which pictures are shown, modify the `idx` argument passed to predict. Should be between \\[0, len(data) = 1500)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8ca43c34a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8d18487be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8b828d10b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def predict(model, dataset, idx, name):\n",
    "    fig, axes = plt.subplots(1, 2)\n",
    "    axes[0].imshow(dataset[idx])\n",
    "    axes[1].imshow(model.predict(np.expand_dims(dataset[idx], 0))[0])\n",
    "    for ax in axes:\n",
    "        ax.set_yticks([])\n",
    "        ax.set_xticks([])\n",
    "        \n",
    "    fig.savefig(name, dpi=70)\n",
    "    plt.show()\n",
    "\n",
    "for model, data, name in zip(models, [android_data, ios_data, web_data], ['android', 'ios', 'web']):\n",
    "    predict(model, data, 100, '{}_hydra.png'.format(name))\n",
    "    model.save('{}-hydra-model'.format(name))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Verifying that the models saved properly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "android-hydra-model  ios-hydra-model  web-hydra-model\r\n"
     ]
    }
   ],
   "source": [
    "% ls *-model"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "PyTorch Kernel",
   "language": "python",
   "name": "torchkern"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}