src/autoencoder_boson_sampling.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Autoencoder for Boson Sampling Experiments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pickle\n",
    "import tensorflow as tf\n",
    "\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator, load_img, save_img, img_to_array\n",
    "from tensorflow.keras.layers import Input, Flatten, Dense, Conv2D\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.optimizers import Adam, SGD\n",
    "from tensorflow.keras.utils import to_categorical\n",
    "\n",
    "import matplotlib.pyplot as plt; plt.rcdefaults()\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from models.AED import Autoencoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "../run/vae/0001_boson/\n"
     ]
    }
   ],
   "source": [
    "# run params\n",
    "SECTION = 'vae'\n",
    "RUN_ID = '0001'\n",
    "DATA_NAME = 'boson/'\n",
    "RUN_FOLDER = '../run/{}/'.format(SECTION)\n",
    "RUN_FOLDER += '_'.join([RUN_ID, DATA_NAME])\n",
    "print(RUN_FOLDER)\n",
    "\n",
    "if not os.path.exists(RUN_FOLDER):\n",
    "    os.makedirs(RUN_FOLDER)\n",
    "    os.mkdir(os.path.join(RUN_FOLDER, 'viz'))\n",
    "    os.mkdir(os.path.join(RUN_FOLDER, 'images'))\n",
    "    os.mkdir(os.path.join(RUN_FOLDER, 'weights'))\n",
    "\n",
    "MODE =  'build' #'load' #"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "modes   = 5\n",
    "photons = 3\n",
    "\n",
    "# load previously generated data from the Boson data generator\n",
    "data_file = '../data/' + str(modes) + '_' + str(photons) + '_data.sav'\n",
    "\n",
    "with open(data_file, \"rb\") as f:\n",
    "    X, Y = pickle.load(f)\n",
    "    \n",
    "X = np.asarray(X)\n",
    "Y = np.asarray(Y)\n",
    "\n",
    "split = round(len(X)*.9)\n",
    "\n",
    "x_train = X[:split]\n",
    "y_train = Y[:split]\n",
    "\n",
    "x_test  = X[split:]\n",
    "y_test  = Y[split:]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "AE = Autoencoder(\n",
    "    input_dim = (x_train.shape[1],)\n",
    "    , encoder_dense_layers = [64,64,32,128]\n",
    "    , decoder_dense_layers = [64,64,32, 35]\n",
    "    , z_dim = 16\n",
    "    , use_dropout = False\n",
    ")\n",
    "\n",
    "if MODE == 'build':\n",
    "    AE.save(RUN_FOLDER)\n",
    "else:\n",
    "    AE.load_weights(os.path.join(RUN_FOLDER, 'weights/weights.h5'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_9\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "encoder_input (InputLayer)   [(None, 25)]              0         \n",
      "_________________________________________________________________\n",
      "encoder_dense_0 (Dense)      (None, 64)                1664      \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_21 (LeakyReLU)   (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "encoder_dense_1 (Dense)      (None, 64)                4160      \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_22 (LeakyReLU)   (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "encoder_dense_2 (Dense)      (None, 32)                2080      \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_23 (LeakyReLU)   (None, 32)                0         \n",
      "_________________________________________________________________\n",
      "encoder_dense_3 (Dense)      (None, 128)               4224      \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_24 (LeakyReLU)   (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "encoder_output (Dense)       (None, 16)                2064      \n",
      "=================================================================\n",
      "Total params: 14,192\n",
      "Trainable params: 14,192\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "AE.encoder.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_10\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "decoder_input (InputLayer)   [(None, 16)]              0         \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 128)               2176      \n",
      "_________________________________________________________________\n",
      "reshape_3 (Reshape)          (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "decoder_dense_t_0 (Dense)    (None, 64)                8256      \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_25 (LeakyReLU)   (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "decoder_dense_t_1 (Dense)    (None, 64)                4160      \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_26 (LeakyReLU)   (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "decoder_dense_t_2 (Dense)    (None, 32)                2080      \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_27 (LeakyReLU)   (None, 32)                0         \n",
      "_________________________________________________________________\n",
      "decoder_dense_t_3 (Dense)    (None, 35)                1155      \n",
      "_________________________________________________________________\n",
      "activation_3 (Activation)    (None, 35)                0         \n",
      "=================================================================\n",
      "Total params: 17,827\n",
      "Trainable params: 17,827\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "AE.decoder.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "LEARNING_RATE = 0.0003\n",
    "BATCH_SIZE = 32\n",
    "INITIAL_EPOCH = 0\n",
    "\n",
    "AE.compile(LEARNING_RATE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 3600 samples\n",
      "Epoch 1/3000\n",
      "3360/3600 [===========================>..] - ETA: 0s - loss: 0.0636 - mae: 0.1623\n",
      "Epoch 00001: saving model to ../run/vae/0001_boson/weights/weights.h5\n",
      "3600/3600 [==============================] - 1s 266us/sample - loss: 0.0595 - mae: 0.1533\n",
      "Epoch 2/3000\n",
      "3584/3600 [============================>.] - ETA: 0s - loss: 0.0023 - mae: 0.0275\n",
      "Epoch 00002: saving model to ../run/vae/0001_boson/weights/weights.h5\n",
      "3600/3600 [==============================] - 0s 135us/sample - loss: 0.0023 - mae: 0.0275\n",
      "Epoch 3/3000\n",
      "3328/3600 [==========================>...] - ETA: 0s - loss: 0.0022 - mae: 0.0275\n",
      "Epoch 00003: saving model to ../run/vae/0001_boson/weights/weights.h5\n",
      "3600/3600 [==============================] - 0s 129us/sample - loss: 0.0022 - mae: 0.0274\n",
      "Epoch 4/3000\n",
      "3232/3600 [=========================>....] - ETA: 0s - loss: 0.0022 - mae: 0.0272\n",
      "Epoch 00004: saving model to ../run/vae/0001_boson/weights/weights.h5\n",
      "3600/3600 [==============================] - 0s 134us/sample - loss: 0.0022 - mae: 0.0272\n",
      "Epoch 5/3000\n",
      "3264/3600 [==========================>...] - ETA: 0s - loss: 0.0022 - mae: 0.0271\n",
      "Epoch 00005: saving model to ../run/vae/0001_boson/weights/weights.h5\n",
      "3600/3600 [==============================] - 0s 131us/sample - loss: 0.0022 - mae: 0.0272\n",
      "Epoch 6/3000\n",
      "3296/3600 [==========================>...] - ETA: 0s - loss: 0.0022 - mae: 0.0272\n",
      "Epoch 00006: saving model to ../run/vae/0001_boson/weights/weights.h5\n",
      "3600/3600 [==============================] - 0s 131us/sample - loss: 0.0021 - mae: 0.0272\n",
      "Epoch 7/3000\n",
      "3456/3600 [===========================>..] - ETA: 0s - loss: 0.0021 - mae: 0.0271\n",
      "Epoch 00007: saving model to ../run/vae/0001_boson/weights/weights.h5\n",
      "3600/3600 [==============================] - 1s 141us/sample - loss: 0.0021 - mae: 0.0271\n",
      "Epoch 8/3000\n",
      "3392/3600 [===========================>..] - ETA: 0s - loss: 0.0021 - mae: 0.0270\n",
      "Epoch 00008: saving model to ../run/vae/0001_boson/weights/weights.h5\n",
      "3600/3600 [==============================] - 1s 145us/sample - loss: 0.0021 - mae: 0.0270\n",
      "Epoch 9/3000\n",
      "3456/3600 [===========================>..] - ETA: 0s - loss: 0.0021 - mae: 0.0271\n",
      "Epoch 00009: saving model to ../run/vae/0001_boson/weights/weights.h5\n",
      "3600/3600 [==============================] - 1s 154us/sample - loss: 0.0021 - mae: 0.0271\n",
      "Epoch 10/3000\n",
      "3456/3600 [===========================>..] - ETA: 0s - loss: 0.0021 - mae: 0.0270\n",
      "Epoch 00010: saving model to ../run/vae/0001_boson/weights/weights.h5\n",
      "3600/3600 [==============================] - 1s 157us/sample - loss: 0.0021 - mae: 0.0270\n",
      "Epoch 11/3000\n",
      "3584/3600 [============================>.] - ETA: 0s - loss: 0.0021 - mae: 0.0271\n",
      "Epoch 00011: saving model to ../run/vae/0001_boson/weights/weights.h5\n",
      "3600/3600 [==============================] - 1s 149us/sample - loss: 0.0021 - mae: 0.0271\n",
      "Epoch 12/3000\n",
      "3392/3600 [===========================>..] - ETA: 0s - loss: 0.0021 - mae: 0.0270\n",
      "Epoch 00012: saving model to ../run/vae/0001_boson/weights/weights.h5\n",
      "3600/3600 [==============================] - 1s 158us/sample - loss: 0.0021 - mae: 0.0271\n",
      "Epoch 13/3000\n",
      "3392/3600 [===========================>..] - ETA: 0s - loss: 0.0020 - mae: 0.0271\n",
      "Epoch 00013: saving model to ../run/vae/0001_boson/weights/weights.h5\n",
      "3600/3600 [==============================] - 1s 161us/sample - loss: 0.0020 - mae: 0.0271\n",
      "Epoch 14/3000\n",
      "3584/3600 [============================>.] - ETA: 0s - loss: 0.0020 - mae: 0.0269\n",
      "Epoch 00014: saving model to ../run/vae/0001_boson/weights/weights.h5\n",
      "3600/3600 [==============================] - 1s 172us/sample - loss: 0.0020 - mae: 0.0269\n",
      "Epoch 15/3000\n",
      "3520/3600 [============================>.] - ETA: 0s - loss: 0.0020 - mae: 0.0268\n",
      "Epoch 00015: saving model to ../run/vae/0001_boson/weights/weights.h5\n",
      "3600/3600 [==============================] - 1s 171us/sample - loss: 0.0020 - mae: 0.0268\n",
      "Epoch 16/3000\n",
      "3328/3600 [==========================>...] - ETA: 0s - loss: 0.0020 - mae: 0.0267\n",
      "Epoch 00016: saving model to ../run/vae/0001_boson/weights/weights.h5\n",
      "3600/3600 [==============================] - 1s 161us/sample - loss: 0.0020 - mae: 0.0267\n",
      "Epoch 17/3000\n",
      "3520/3600 [============================>.] - ETA: 0s - loss: 0.0019 - mae: 0.0265\n",
      "Epoch 00017: saving model to ../run/vae/0001_boson/weights/weights.h5\n",
      "3600/3600 [==============================] - 1s 171us/sample - loss: 0.0019 - mae: 0.0266\n",
      "Epoch 18/3000\n",
      "3296/3600 [==========================>...] - ETA: 0s - loss: 0.0019 - mae: 0.0263\n",
      "Epoch 00018: saving model to ../run/vae/0001_boson/weights/weights.h5\n",
      "3600/3600 [==============================] - 1s 166us/sample - loss: 0.0019 - mae: 0.0263\n",
      "Epoch 19/3000\n",
      "3584/3600 [============================>.] - ETA: 0s - loss: 0.0019 - mae: 0.0260\n",
      "Epoch 00019: saving model to ../run/vae/0001_boson/weights/weights.h5\n",
      "3600/3600 [==============================] - 1s 167us/sample - loss: 0.0019 - mae: 0.0260\n",
      "Epoch 20/3000\n",
      "3520/3600 [============================>.] - ETA: 0s - loss: 0.0019 - mae: 0.0259\n",
      "Epoch 00020: saving model to ../run/vae/0001_boson/weights/weights.h5\n",
      "3600/3600 [==============================] - 1s 173us/sample - loss: 0.0019 - mae: 0.0258\n",
      "Epoch 21/3000\n",
      "3360/3600 [===========================>..] - ETA: 0s - loss: 0.0018 - mae: 0.0257\n",
      "Epoch 00021: saving model to ../run/vae/0001_boson/weights/weights.h5\n",
      "3600/3600 [==============================] - 1s 179us/sample - loss: 0.0018 - mae: 0.0257\n",
      "Epoch 22/3000\n",
      "3488/3600 [============================>.] - ETA: 0s - loss: 0.0018 - mae: 0.0256\n",
      "Epoch 00022: saving model to ../run/vae/0001_boson/weights/weights.h5\n",
      "3600/3600 [==============================] - 1s 266us/sample - loss: 0.0018 - mae: 0.0256\n",
      "Epoch 23/3000\n",
      "3584/3600 [============================>.] - ETA: 0s - loss: 0.0018 - mae: 0.0255\n",
      "Epoch 00023: saving model to ../run/vae/0001_boson/weights/weights.h5\n",
      "3600/3600 [==============================] - 1s 243us/sample - loss: 0.0018 - mae: 0.0255\n",
      "Epoch 24/3000\n",
      "3520/3600 [============================>.] - ETA: 0s - loss: 0.0018 - mae: 0.0253\n",
      "Epoch 00024: saving model to ../run/vae/0001_boson/weights/weights.h5\n",
      "3600/3600 [==============================] - 1s 240us/sample - loss: 0.0018 - mae: 0.0253\n",
      "Epoch 25/3000\n",
      "3520/3600 [============================>.] - ETA: 0s - loss: 0.0017 - mae: 0.0250\n",
      "Epoch 00025: saving model to ../run/vae/0001_boson/weights/weights.h5\n",
      "3600/3600 [==============================] - 1s 252us/sample - loss: 0.0017 - mae: 0.0250\n",
      "Epoch 26/3000\n",
      "3584/3600 [============================>.] - ETA: 0s - loss: 0.0017 - mae: 0.0248\n",
      "Epoch 00026: saving model to ../run/vae/0001_boson/weights/weights.h5\n",
      "3600/3600 [==============================] - 1s 236us/sample - loss: 0.0017 - mae: 0.0248\n",
      "Epoch 27/3000\n",
      "3456/3600 [===========================>..] - ETA: 0s - loss: 0.0017 - mae: 0.0247\n",
      "Epoch 00027: saving model to ../run/vae/0001_boson/weights/weights.h5\n",
      "3600/3600 [==============================] - 1s 226us/sample - loss: 0.0017 - mae: 0.0247\n",
      "Epoch 28/3000\n",
      "3520/3600 [============================>.] - ETA: 0s - loss: 0.0017 - mae: 0.0246\n",
      "Epoch 00028: saving model to ../run/vae/0001_boson/weights/weights.h5\n",
      "3600/3600 [==============================] - 1s 188us/sample - loss: 0.0017 - mae: 0.0246\n",
      "Epoch 29/3000\n",
      "3552/3600 [============================>.] - ETA: 0s - loss: 0.0016 - mae: 0.0244\n",
      "Epoch 00029: saving model to ../run/vae/0001_boson/weights/weights.h5\n",
      "3600/3600 [==============================] - 1s 261us/sample - loss: 0.0016 - mae: 0.0244\n",
      "Epoch 30/3000\n",
      "3456/3600 [===========================>..] - ETA: 0s - loss: 0.0016 - mae: 0.0244\n",
      "Epoch 00030: saving model to ../run/vae/0001_boson/weights/weights.h5\n",
      "3600/3600 [==============================] - 1s 373us/sample - loss: 0.0016 - mae: 0.0243\n",
      "Epoch 31/3000\n",
      "3552/3600 [============================>.] - ETA: 0s - loss: 0.0016 - mae: 0.0243\n",
      "Epoch 00031: saving model to ../run/vae/0001_boson/weights/weights.h5\n",
      "3600/3600 [==============================] - 1s 260us/sample - loss: 0.0016 - mae: 0.0243\n",
      "Epoch 32/3000\n",
      "3520/3600 [============================>.] - ETA: 0s - loss: 0.0016 - mae: 0.0242\n",
      "Epoch 00032: saving model to ../run/vae/0001_boson/weights/weights.h5\n",
      "3600/3600 [==============================] - 1s 263us/sample - loss: 0.0016 - mae: 0.0242\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 33/3000\n",
      "3424/3600 [===========================>..] - ETA: 0s - loss: 0.0016 - mae: 0.0241\n",
      "Epoch 00033: saving model to ../run/vae/0001_boson/weights/weights.h5\n",
      "3600/3600 [==============================] - 1s 209us/sample - loss: 0.0016 - mae: 0.0241\n",
      "Epoch 34/3000\n",
      "3488/3600 [============================>.] - ETA: 0s - loss: 0.0015 - mae: 0.0238\n",
      "Epoch 00034: saving model to ../run/vae/0001_boson/weights/weights.h5\n",
      "3600/3600 [==============================] - 1s 250us/sample - loss: 0.0015 - mae: 0.0238\n",
      "Epoch 35/3000\n",
      "3584/3600 [============================>.] - ETA: 0s - loss: 0.0014 - mae: 0.0234\n",
      "Epoch 00035: saving model to ../run/vae/0001_boson/weights/weights.h5\n",
      "3600/3600 [==============================] - 1s 386us/sample - loss: 0.0014 - mae: 0.0234\n",
      "Epoch 36/3000\n",
      "3488/3600 [============================>.] - ETA: 0s - loss: 0.0014 - mae: 0.0232\n",
      "Epoch 00036: saving model to ../run/vae/0001_boson/weights/weights.h5\n",
      "3600/3600 [==============================] - 1s 270us/sample - loss: 0.0014 - mae: 0.0232\n",
      "Epoch 37/3000\n",
      "3424/3600 [===========================>..] - ETA: 0s - loss: 0.0014 - mae: 0.0230\n",
      "Epoch 00037: saving model to ../run/vae/0001_boson/weights/weights.h5\n",
      "3600/3600 [==============================] - 1s 272us/sample - loss: 0.0014 - mae: 0.0230\n",
      "Epoch 38/3000\n",
      "3392/3600 [===========================>..] - ETA: 0s - loss: 0.0014 - mae: 0.0228\n",
      "Epoch 00038: saving model to ../run/vae/0001_boson/weights/weights.h5\n",
      "3600/3600 [==============================] - 1s 175us/sample - loss: 0.0014 - mae: 0.0228\n",
      "Epoch 39/3000\n",
      "3328/3600 [==========================>...] - ETA: 0s - loss: 0.0013 - mae: 0.0226\n",
      "Epoch 00039: saving model to ../run/vae/0001_boson/weights/weights.h5\n",
      "3600/3600 [==============================] - 1s 179us/sample - loss: 0.0013 - mae: 0.0225\n",
      "Epoch 40/3000\n",
      "3360/3600 [===========================>..] - ETA: 0s - loss: 0.0013 - mae: 0.0223\n",
      "Epoch 00040: saving model to ../run/vae/0001_boson/weights/weights.h5\n",
      "3600/3600 [==============================] - 1s 177us/sample - loss: 0.0013 - mae: 0.0223\n",
      "Epoch 41/3000\n",
      "3392/3600 [===========================>..] - ETA: 0s - loss: 0.0013 - mae: 0.0220\n",
      "Epoch 00041: saving model to ../run/vae/0001_boson/weights/weights.h5\n",
      "3600/3600 [==============================] - 1s 177us/sample - loss: 0.0013 - mae: 0.0220\n",
      "Epoch 42/3000\n",
      "3456/3600 [===========================>..] - ETA: 0s - loss: 0.0013 - mae: 0.0218\n",
      "Epoch 00042: saving model to ../run/vae/0001_boson/weights/weights.h5\n",
      "3600/3600 [==============================] - 1s 193us/sample - loss: 0.0013 - mae: 0.0218\n",
      "Epoch 43/3000\n",
      "3424/3600 [===========================>..] - ETA: 0s - loss: 0.0012 - mae: 0.0216\n",
      "Epoch 00043: saving model to ../run/vae/0001_boson/weights/weights.h5\n",
      "3600/3600 [==============================] - 1s 216us/sample - loss: 0.0012 - mae: 0.0216\n",
      "Epoch 44/3000\n",
      "3328/3600 [==========================>...] - ETA: 0s - loss: 0.0012 - mae: 0.0213- ETA: 1s - loss: 0.001\n",
      "Epoch 00044: saving model to ../run/vae/0001_boson/weights/weights.h5\n",
      "3600/3600 [==============================] - 1s 284us/sample - loss: 0.0012 - mae: 0.0213\n",
      "Epoch 45/3000\n",
      "3424/3600 [===========================>..] - ETA: 0s - loss: 0.0012 - mae: 0.0212\n",
      "Epoch 00045: saving model to ../run/vae/0001_boson/weights/weights.h5\n",
      "3600/3600 [==============================] - 1s 203us/sample - loss: 0.0012 - mae: 0.0212\n",
      "Epoch 46/3000\n",
      "3328/3600 [==========================>...] - ETA: 0s - loss: 0.0012 - mae: 0.0209\n",
      "Epoch 00046: saving model to ../run/vae/0001_boson/weights/weights.h5\n",
      "3600/3600 [==============================] - 1s 178us/sample - loss: 0.0012 - mae: 0.0210\n",
      "Epoch 47/3000\n",
      "3488/3600 [============================>.] - ETA: 0s - loss: 0.0012 - mae: 0.0208\n",
      "Epoch 00047: saving model to ../run/vae/0001_boson/weights/weights.h5\n",
      "3600/3600 [==============================] - 1s 191us/sample - loss: 0.0012 - mae: 0.0208\n",
      "Epoch 48/3000\n",
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      "3600/3600 [==============================] - 1s 180us/sample - loss: 0.0011 - mae: 0.0207\n",
      "Epoch 49/3000\n",
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      "3600/3600 [==============================] - 1s 208us/sample - loss: 0.0011 - mae: 0.0204\n",
      "Epoch 50/3000\n",
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      "3600/3600 [==============================] - 1s 187us/sample - loss: 0.0011 - mae: 0.0203\n",
      "Epoch 51/3000\n",
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      "3600/3600 [==============================] - 1s 223us/sample - loss: 0.0011 - mae: 0.0199\n",
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      "3600/3600 [==============================] - 1s 234us/sample - loss: 0.0010 - mae: 0.0194\n",
      "Epoch 56/3000\n",
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      "3600/3600 [==============================] - 1s 274us/sample - loss: 9.8908e-04 - mae: 0.0192\n",
      "Epoch 58/3000\n",
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      "3600/3600 [==============================] - 1s 334us/sample - loss: 9.7755e-04 - mae: 0.0191\n",
      "Epoch 59/3000\n",
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      "3600/3600 [==============================] - 1s 339us/sample - loss: 9.6139e-04 - mae: 0.0189\n",
      "Epoch 60/3000\n",
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      "3600/3600 [==============================] - 1s 375us/sample - loss: 9.5719e-04 - mae: 0.0188\n",
      "Epoch 61/3000\n",
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      "3600/3600 [==============================] - 1s 202us/sample - loss: 9.2558e-04 - mae: 0.0185\n",
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      "3600/3600 [==============================] - 1s 184us/sample - loss: 9.1096e-04 - mae: 0.0183\n",
      "Epoch 64/3000\n",
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      "3600/3600 [==============================] - 1s 178us/sample - loss: 8.9470e-04 - mae: 0.0181\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 65/3000\n",
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      "3600/3600 [==============================] - 1s 186us/sample - loss: 8.8986e-04 - mae: 0.0180\n",
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      "3600/3600 [==============================] - 1s 222us/sample - loss: 8.8115e-04 - mae: 0.0179\n",
      "Epoch 67/3000\n",
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      "3600/3600 [==============================] - 1s 204us/sample - loss: 8.5278e-04 - mae: 0.0176\n",
      "Epoch 69/3000\n",
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      "Epoch 70/3000\n",
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      "3600/3600 [==============================] - 1s 187us/sample - loss: 8.2935e-04 - mae: 0.0174\n",
      "Epoch 71/3000\n",
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      "3600/3600 [==============================] - 1s 181us/sample - loss: 8.2149e-04 - mae: 0.0173\n",
      "Epoch 72/3000\n",
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      "3600/3600 [==============================] - 1s 255us/sample - loss: 8.1043e-04 - mae: 0.0171\n",
      "Epoch 73/3000\n",
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      "3600/3600 [==============================] - 1s 183us/sample - loss: 7.8510e-04 - mae: 0.0169\n",
      "Epoch 75/3000\n",
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      "3600/3600 [==============================] - 1s 173us/sample - loss: 7.8261e-04 - mae: 0.0168\n",
      "Epoch 76/3000\n",
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      "3600/3600 [==============================] - 1s 189us/sample - loss: 7.7041e-04 - mae: 0.0168\n",
      "Epoch 77/3000\n",
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      "3600/3600 [==============================] - 1s 203us/sample - loss: 7.5500e-04 - mae: 0.0166\n",
      "Epoch 78/3000\n",
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      "3600/3600 [==============================] - 1s 229us/sample - loss: 7.5182e-04 - mae: 0.0166\n",
      "Epoch 79/3000\n",
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      "3600/3600 [==============================] - 1s 193us/sample - loss: 6.8316e-04 - mae: 0.0157\n",
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      "Epoch 96/3000\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 127/3000\n",
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      "3600/3600 [==============================] - 1s 226us/sample - loss: 4.5459e-04 - mae: 0.0125\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 158/3000\n",
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      "Epoch 184/3000\n",
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      "Epoch 186/3000\n",
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      "Epoch 187/3000\n",
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      "Epoch 188/3000\n",
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      "3600/3600 [==============================] - 1s 184us/sample - loss: 3.4925e-04 - mae: 0.0110\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 189/3000\n",
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      "3600/3600 [==============================] - 1s 201us/sample - loss: 3.4416e-04 - mae: 0.0109\n",
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      "3600/3600 [==============================] - 1s 198us/sample - loss: 3.4735e-04 - mae: 0.0110\n",
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      "3600/3600 [==============================] - 1s 187us/sample - loss: 3.4467e-04 - mae: 0.0109\n",
      "Epoch 192/3000\n",
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      "3600/3600 [==============================] - 1s 223us/sample - loss: 3.4584e-04 - mae: 0.0110\n",
      "Epoch 193/3000\n",
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      "Epoch 197/3000\n",
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      "3600/3600 [==============================] - 1s 211us/sample - loss: 3.3433e-04 - mae: 0.0108\n",
      "Epoch 199/3000\n",
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      "3600/3600 [==============================] - 1s 184us/sample - loss: 3.3192e-04 - mae: 0.0108\n",
      "Epoch 200/3000\n",
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      "3600/3600 [==============================] - 1s 179us/sample - loss: 3.3361e-04 - mae: 0.0109\n",
      "Epoch 201/3000\n",
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      "Epoch 202/3000\n",
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      "3600/3600 [==============================] - 1s 196us/sample - loss: 3.1849e-04 - mae: 0.0106\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 220/3000\n",
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      "Epoch 244/3000\n",
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      "Epoch 245/3000\n",
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      "Epoch 246/3000\n",
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      "3600/3600 [==============================] - 1s 227us/sample - loss: 2.7886e-04 - mae: 0.0100\n",
      "Epoch 247/3000\n",
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      "Epoch 248/3000\n",
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      "3600/3600 [==============================] - 1s 194us/sample - loss: 2.7125e-04 - mae: 0.0099\n",
      "Epoch 249/3000\n",
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      "3600/3600 [==============================] - 1s 210us/sample - loss: 2.7046e-04 - mae: 0.0099\n",
      "Epoch 250/3000\n",
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      "3600/3600 [==============================] - 1s 196us/sample - loss: 2.7494e-04 - mae: 0.0099\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 251/3000\n",
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      "3600/3600 [==============================] - 1s 222us/sample - loss: 2.7363e-04 - mae: 0.0099\n",
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      "3600/3600 [==============================] - 1s 196us/sample - loss: 2.7117e-04 - mae: 0.0099\n",
      "Epoch 253/3000\n",
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      "3600/3600 [==============================] - 1s 201us/sample - loss: 2.6264e-04 - mae: 0.0097\n",
      "Epoch 254/3000\n",
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      "3600/3600 [==============================] - 1s 189us/sample - loss: 2.6597e-04 - mae: 0.0098\n",
      "Epoch 255/3000\n",
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      "3600/3600 [==============================] - 1s 245us/sample - loss: 2.7778e-04 - mae: 0.0099\n",
      "Epoch 256/3000\n",
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      "Epoch 257/3000\n",
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      "3600/3600 [==============================] - 1s 187us/sample - loss: 2.6742e-04 - mae: 0.0098\n",
      "Epoch 259/3000\n",
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      "3600/3600 [==============================] - 1s 184us/sample - loss: 2.6217e-04 - mae: 0.0097\n",
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      "3600/3600 [==============================] - 1s 189us/sample - loss: 2.6159e-04 - mae: 0.0097\n",
      "Epoch 261/3000\n",
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      "3600/3600 [==============================] - 1s 221us/sample - loss: 2.6450e-04 - mae: 0.0097\n",
      "Epoch 262/3000\n",
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      "3600/3600 [==============================] - 1s 201us/sample - loss: 2.6039e-04 - mae: 0.0097\n",
      "Epoch 263/3000\n",
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      "3600/3600 [==============================] - 1s 201us/sample - loss: 2.5983e-04 - mae: 0.0097\n",
      "Epoch 264/3000\n",
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      "Epoch 265/3000\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 282/3000\n",
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      "3600/3600 [==============================] - 1s 189us/sample - loss: 2.4040e-04 - mae: 0.0093\n",
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      "Epoch 310/3000\n",
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      "Epoch 311/3000\n",
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      "Epoch 312/3000\n",
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      "3600/3600 [==============================] - 1s 175us/sample - loss: 2.3818e-04 - mae: 0.0092\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 313/3000\n",
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      "3600/3600 [==============================] - 1s 173us/sample - loss: 2.3517e-04 - mae: 0.0092\n",
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      "3600/3600 [==============================] - 1s 180us/sample - loss: 2.3440e-04 - mae: 0.0092\n",
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      "3600/3600 [==============================] - 1s 177us/sample - loss: 2.3356e-04 - mae: 0.0092\n",
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      "3600/3600 [==============================] - 1s 201us/sample - loss: 2.3238e-04 - mae: 0.0092\n",
      "Epoch 324/3000\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 344/3000\n",
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      "3600/3600 [==============================] - 1s 239us/sample - loss: 2.0999e-04 - mae: 0.0088\n",
      "Epoch 374/3000\n",
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      "3600/3600 [==============================] - 1s 284us/sample - loss: 2.0624e-04 - mae: 0.0087\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 375/3000\n",
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      "Epoch 00375: saving model to ../run/vae/0001_boson/weights/weights.h5\n",
      "3600/3600 [==============================] - 1s 268us/sample - loss: 2.0761e-04 - mae: 0.0087\n",
      "Epoch 376/3000\n",
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      "3600/3600 [==============================] - 1s 187us/sample - loss: 2.1118e-04 - mae: 0.0088\n",
      "Epoch 377/3000\n",
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      "3600/3600 [==============================] - 1s 188us/sample - loss: 2.1067e-04 - mae: 0.0088\n",
      "Epoch 378/3000\n",
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      "3600/3600 [==============================] - 1s 174us/sample - loss: 2.0625e-04 - mae: 0.0087\n",
      "Epoch 379/3000\n",
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      "3600/3600 [==============================] - 1s 184us/sample - loss: 2.1052e-04 - mae: 0.0088\n",
      "Epoch 380/3000\n",
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      "3600/3600 [==============================] - 1s 174us/sample - loss: 2.0958e-04 - mae: 0.0087\n",
      "Epoch 381/3000\n",
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      "3600/3600 [==============================] - 1s 182us/sample - loss: 2.0291e-04 - mae: 0.0086\n",
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      "3600/3600 [==============================] - 1s 184us/sample - loss: 2.0244e-04 - mae: 0.0086\n",
      "Epoch 383/3000\n",
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      "Epoch 384/3000\n",
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      "3600/3600 [==============================] - 1s 187us/sample - loss: 2.0396e-04 - mae: 0.0087\n",
      "Epoch 385/3000\n",
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      "3600/3600 [==============================] - 1s 204us/sample - loss: 2.0439e-04 - mae: 0.0086\n",
      "Epoch 386/3000\n",
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      "Epoch 387/3000\n",
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      "Epoch 388/3000\n",
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      "3600/3600 [==============================] - 1s 189us/sample - loss: 2.0401e-04 - mae: 0.0086\n",
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      "Epoch 390/3000\n",
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      "3600/3600 [==============================] - 1s 190us/sample - loss: 2.0101e-04 - mae: 0.0086\n",
      "Epoch 391/3000\n",
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      "Epoch 400/3000\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 406/3000\n",
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      "3600/3600 [==============================] - 1s 169us/sample - loss: 1.9959e-04 - mae: 0.0085\n",
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     ]
    },
    {
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     "text": [
      "Epoch 437/3000\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 468/3000\n",
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     ]
    },
    {
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     "text": [
      "Epoch 499/3000\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 530/3000\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 561/3000\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 592/3000\n",
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     ]
    },
    {
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     "text": [
      "Epoch 623/3000\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 654/3000\n",
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     ]
    },
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     "text": [
      "Epoch 685/3000\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 716/3000\n",
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     ]
    },
    {
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     "text": [
      "Epoch 747/3000\n",
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      "3600/3600 [==============================] - 1s 194us/sample - loss: 1.3046e-04 - mae: 0.0071\n",
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      "3600/3600 [==============================] - 1s 277us/sample - loss: 1.3765e-04 - mae: 0.0072\n",
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      "3600/3600 [==============================] - 1s 234us/sample - loss: 1.3056e-04 - mae: 0.0071\n",
      "Epoch 772/3000\n",
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      "3600/3600 [==============================] - 1s 278us/sample - loss: 1.3322e-04 - mae: 0.0071\n",
      "Epoch 773/3000\n",
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      "3600/3600 [==============================] - 1s 360us/sample - loss: 1.3252e-04 - mae: 0.0071\n",
      "Epoch 774/3000\n",
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      "3600/3600 [==============================] - 1s 265us/sample - loss: 1.3113e-04 - mae: 0.0071\n",
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      "3600/3600 [==============================] - 1s 222us/sample - loss: 1.2934e-04 - mae: 0.0070\n",
      "Epoch 776/3000\n",
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      "3600/3600 [==============================] - 1s 191us/sample - loss: 1.2934e-04 - mae: 0.0070\n",
      "Epoch 777/3000\n",
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      "3600/3600 [==============================] - 1s 191us/sample - loss: 1.3023e-04 - mae: 0.0071\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 778/3000\n",
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      "Epoch 00778: saving model to ../run/vae/0001_boson/weights/weights.h5\n",
      "3600/3600 [==============================] - 1s 186us/sample - loss: 1.2937e-04 - mae: 0.0070\n",
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      "3600/3600 [==============================] - 1s 190us/sample - loss: 1.2954e-04 - mae: 0.0070\n",
      "Epoch 780/3000\n",
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      "3600/3600 [==============================] - 1s 206us/sample - loss: 1.2949e-04 - mae: 0.0070\n",
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      "3600/3600 [==============================] - 1s 199us/sample - loss: 1.2951e-04 - mae: 0.0070\n",
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      "3600/3600 [==============================] - 1s 202us/sample - loss: 1.3544e-04 - mae: 0.0072\n",
      "Epoch 783/3000\n",
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      "3600/3600 [==============================] - 1s 196us/sample - loss: 1.2817e-04 - mae: 0.0070\n",
      "Epoch 784/3000\n",
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      "3600/3600 [==============================] - 1s 193us/sample - loss: 1.3244e-04 - mae: 0.0071\n",
      "Epoch 785/3000\n",
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      "3600/3600 [==============================] - 1s 218us/sample - loss: 1.3278e-04 - mae: 0.0071\n",
      "Epoch 786/3000\n",
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      "3600/3600 [==============================] - 1s 193us/sample - loss: 1.3034e-04 - mae: 0.0071\n",
      "Epoch 787/3000\n",
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      "3600/3600 [==============================] - 1s 198us/sample - loss: 1.2929e-04 - mae: 0.0070\n",
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      "3600/3600 [==============================] - 1s 183us/sample - loss: 1.2984e-04 - mae: 0.0070\n",
      "Epoch 789/3000\n",
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      "3600/3600 [==============================] - 1s 189us/sample - loss: 1.2902e-04 - mae: 0.0070\n",
      "Epoch 790/3000\n",
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      "Epoch 00790: saving model to ../run/vae/0001_boson/weights/weights.h5\n",
      "3600/3600 [==============================] - 1s 204us/sample - loss: 1.2796e-04 - mae: 0.0070\n",
      "Epoch 791/3000\n",
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      "3600/3600 [==============================] - 1s 210us/sample - loss: 1.3087e-04 - mae: 0.0071\n",
      "Epoch 792/3000\n",
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      "Epoch 00792: saving model to ../run/vae/0001_boson/weights/weights.h5\n",
      "3600/3600 [==============================] - 1s 193us/sample - loss: 1.3006e-04 - mae: 0.0070\n",
      "Epoch 793/3000\n",
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      "3600/3600 [==============================] - 1s 194us/sample - loss: 1.2982e-04 - mae: 0.0070\n",
      "Epoch 794/3000\n",
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      "3600/3600 [==============================] - 1s 191us/sample - loss: 1.2711e-04 - mae: 0.0070\n",
      "Epoch 795/3000\n",
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      "3600/3600 [==============================] - 1s 183us/sample - loss: 1.2979e-04 - mae: 0.0070\n",
      "Epoch 796/3000\n",
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      "3600/3600 [==============================] - 1s 193us/sample - loss: 1.2971e-04 - mae: 0.0070\n",
      "Epoch 797/3000\n",
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      "3600/3600 [==============================] - 1s 189us/sample - loss: 1.3476e-04 - mae: 0.0071\n",
      "Epoch 798/3000\n",
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      "3600/3600 [==============================] - 1s 185us/sample - loss: 1.2760e-04 - mae: 0.0070\n",
      "Epoch 799/3000\n",
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      "Epoch 00799: saving model to ../run/vae/0001_boson/weights/weights.h5\n",
      "3600/3600 [==============================] - 1s 192us/sample - loss: 1.2848e-04 - mae: 0.0070\n",
      "Epoch 800/3000\n",
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      "3600/3600 [==============================] - 1s 218us/sample - loss: 1.2698e-04 - mae: 0.0070\n",
      "Epoch 801/3000\n",
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     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-39-0bdbd73a56ab>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      4\u001b[0m     \u001b[1;33m,\u001b[0m \u001b[0mepochs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m3000\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m     \u001b[1;33m,\u001b[0m \u001b[0mrun_folder\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mRUN_FOLDER\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m     \u001b[1;33m,\u001b[0m \u001b[0minitial_epoch\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mINITIAL_EPOCH\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      7\u001b[0m )\n\u001b[0;32m      8\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mE:\\generative-boson-sampling\\src\\models\\AED.py\u001b[0m in \u001b[0;36mtrain\u001b[1;34m(self, x_train, y_train, batch_size, epochs, run_folder, print_every_n_batches, initial_epoch, lr_decay)\u001b[0m\n\u001b[0;32m    157\u001b[0m             \u001b[1;33m,\u001b[0m \u001b[0mepochs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mepochs\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    158\u001b[0m             \u001b[1;33m,\u001b[0m \u001b[0minitial_epoch\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0minitial_epoch\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 159\u001b[1;33m             \u001b[1;33m,\u001b[0m \u001b[0mcallbacks\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcallbacks_list\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    160\u001b[0m         )\n\u001b[0;32m    161\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32me:\\python35\\lib\\site-packages\\tensorflow_core\\python\\keras\\engine\\training.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, max_queue_size, workers, use_multiprocessing, **kwargs)\u001b[0m\n\u001b[0;32m    817\u001b[0m         \u001b[0mmax_queue_size\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmax_queue_size\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    818\u001b[0m         \u001b[0mworkers\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mworkers\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 819\u001b[1;33m         use_multiprocessing=use_multiprocessing)\n\u001b[0m\u001b[0;32m    820\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    821\u001b[0m   def evaluate(self,\n",
      "\u001b[1;32me:\\python35\\lib\\site-packages\\tensorflow_core\\python\\keras\\engine\\training_v2.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, model, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, max_queue_size, workers, use_multiprocessing, **kwargs)\u001b[0m\n\u001b[0;32m    340\u001b[0m                 \u001b[0mmode\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mModeKeys\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTRAIN\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    341\u001b[0m                 \u001b[0mtraining_context\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtraining_context\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 342\u001b[1;33m                 total_epochs=epochs)\n\u001b[0m\u001b[0;32m    343\u001b[0m             \u001b[0mcbks\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmake_logs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mepoch_logs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtraining_result\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mModeKeys\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTRAIN\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    344\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32me:\\python35\\lib\\site-packages\\tensorflow_core\\python\\keras\\engine\\training_v2.py\u001b[0m in \u001b[0;36mrun_one_epoch\u001b[1;34m(model, iterator, execution_function, dataset_size, batch_size, strategy, steps_per_epoch, num_samples, mode, training_context, total_epochs)\u001b[0m\n\u001b[0;32m    126\u001b[0m         step=step, mode=mode, size=current_batch_size) as batch_logs:\n\u001b[0;32m    127\u001b[0m       \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 128\u001b[1;33m         \u001b[0mbatch_outs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mexecution_function\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    129\u001b[0m       \u001b[1;32mexcept\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mStopIteration\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mOutOfRangeError\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    130\u001b[0m         \u001b[1;31m# TODO(kaftan): File bug about tf function and errors.OutOfRangeError?\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32me:\\python35\\lib\\site-packages\\tensorflow_core\\python\\keras\\engine\\training_v2_utils.py\u001b[0m in \u001b[0;36mexecution_function\u001b[1;34m(input_fn)\u001b[0m\n\u001b[0;32m     96\u001b[0m     \u001b[1;31m# `numpy` translates Tensors to values in Eager mode.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     97\u001b[0m     return nest.map_structure(_non_none_constant_value,\n\u001b[1;32m---> 98\u001b[1;33m                               distributed_function(input_fn))\n\u001b[0m\u001b[0;32m     99\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    100\u001b[0m   \u001b[1;32mreturn\u001b[0m \u001b[0mexecution_function\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32me:\\python35\\lib\\site-packages\\tensorflow_core\\python\\util\\nest.py\u001b[0m in \u001b[0;36mmap_structure\u001b[1;34m(func, *structure, **kwargs)\u001b[0m\n\u001b[0;32m    566\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    567\u001b[0m   return pack_sequence_as(\n\u001b[1;32m--> 568\u001b[1;33m       \u001b[0mstructure\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mentries\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    569\u001b[0m       expand_composites=expand_composites)\n\u001b[0;32m    570\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32me:\\python35\\lib\\site-packages\\tensorflow_core\\python\\util\\nest.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m    566\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    567\u001b[0m   return pack_sequence_as(\n\u001b[1;32m--> 568\u001b[1;33m       \u001b[0mstructure\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mentries\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    569\u001b[0m       expand_composites=expand_composites)\n\u001b[0;32m    570\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32me:\\python35\\lib\\site-packages\\tensorflow_core\\python\\keras\\engine\\training_v2_utils.py\u001b[0m in \u001b[0;36m_non_none_constant_value\u001b[1;34m(v)\u001b[0m\n\u001b[0;32m    128\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    129\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_non_none_constant_value\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mv\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 130\u001b[1;33m   \u001b[0mconstant_value\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtensor_util\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconstant_value\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mv\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    131\u001b[0m   \u001b[1;32mreturn\u001b[0m \u001b[0mconstant_value\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mconstant_value\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;32melse\u001b[0m \u001b[0mv\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    132\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32me:\\python35\\lib\\site-packages\\tensorflow_core\\python\\framework\\tensor_util.py\u001b[0m in \u001b[0;36mconstant_value\u001b[1;34m(tensor, partial)\u001b[0m\n\u001b[0;32m    820\u001b[0m   \"\"\"\n\u001b[0;32m    821\u001b[0m   \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtensor\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mops\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mEagerTensor\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 822\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mtensor\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    823\u001b[0m   \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mis_tensor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtensor\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    824\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mtensor\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32me:\\python35\\lib\\site-packages\\tensorflow_core\\python\\framework\\ops.py\u001b[0m in \u001b[0;36mnumpy\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    940\u001b[0m     \"\"\"\n\u001b[0;32m    941\u001b[0m     \u001b[1;31m# TODO(slebedev): Consider avoiding a copy for non-CPU or remote tensors.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 942\u001b[1;33m     \u001b[0mmaybe_arr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_numpy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m  \u001b[1;31m# pylint: disable=protected-access\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    943\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mmaybe_arr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmaybe_arr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32melse\u001b[0m \u001b[0mmaybe_arr\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    944\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32me:\\python35\\lib\\site-packages\\tensorflow_core\\python\\framework\\ops.py\u001b[0m in \u001b[0;36m_numpy\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    906\u001b[0m     \u001b[1;31m# pylint: disable=protected-access\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    907\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 908\u001b[1;33m       \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_numpy_internal\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    909\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    910\u001b[0m       \u001b[0msix\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mraise_from\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcore\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_status_to_exception\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcode\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmessage\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "AE.train(     \n",
    "    x_train, y_train\n",
    "    , batch_size = BATCH_SIZE\n",
    "    , epochs = 3000\n",
    "    , run_folder = RUN_FOLDER\n",
    "    , initial_epoch = INITIAL_EPOCH\n",
    ")\n",
    "\n",
    "AE.save(RUN_FOLDER)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_model(model_class, folder):\n",
    "    \n",
    "    with open(os.path.join(folder, 'params.pkl'), 'rb') as f:\n",
    "        params = pickle.load(f)\n",
    "\n",
    "    model = model_class(*params)\n",
    "    model.load_weights(os.path.join(folder, 'weights/weights.h5'))\n",
    "\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "AE = load_model(Autoencoder, RUN_FOLDER)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "z_points:  [[-0.8850612  -1.4595169   0.3192515   3.057255    0.26186588 -0.71518177\n",
      "  -1.1694394   1.1918801  -3.696774    0.20555446 -2.353115    0.32696095\n",
      "  -2.023741   -1.2376521  -1.1473072   0.49997568]\n",
      " [-1.334587   -1.0965701   0.0705612   2.6470816   1.0533707  -0.5266363\n",
      "  -2.5689237   2.018706   -2.8023257   0.01464787 -0.63225985 -0.41749474\n",
      "  -1.1645026  -0.66227853 -0.96209943 -0.420865  ]\n",
      " [-0.04485538  1.2180907   1.1255225   1.3352629   0.683696    0.27580202\n",
      "  -3.0910506   1.7721685  -2.0041761  -0.6098859   0.520736    0.95149237\n",
      "  -0.70703274 -0.11625181 -2.0608928  -0.75183785]\n",
      " [ 1.4862552  -1.0811586   0.22312395  2.7844145  -0.08252653 -1.8557069\n",
      "   1.5337261  -0.8469637  -1.0220857   1.3053411   0.19865714 -3.0475485\n",
      "   1.5087383  -2.7647378  -0.524202   -2.1086423 ]\n",
      " [ 0.3737097  -0.18414801  0.43501517  1.9893588   1.0242053  -0.5426652\n",
      "  -1.9641534   1.7642074  -1.2335712   0.11939108  0.3393382  -1.1560179\n",
      "  -0.06505631 -0.27096397 -1.4062085  -1.6221147 ]\n",
      " [-0.06574822  0.3989034   0.50268304  3.107446   -2.348293   -1.9095409\n",
      "   1.1171718  -2.4896986  -0.6401047   0.9352884   1.182103   -3.114833\n",
      "   1.1286427   0.19679408 -0.41904297 -4.876833  ]\n",
      " [ 1.8980654  -0.7427033   0.24667738  1.6953809   0.39021856 -2.3004918\n",
      "   0.8028222   1.379692   -1.2822678  -0.20642045 -0.48323902 -2.4296167\n",
      "   0.7410269  -2.9895039  -1.5976226  -0.46581876]\n",
      " [ 1.0112664  -0.47646883  1.0975235   1.6544083   1.1979146   0.47169626\n",
      "  -1.5912228   1.671505   -1.7835667   0.64902633  0.02166545 -0.46755752\n",
      "  -0.4167892  -2.3493423  -1.6291213  -0.47865492]\n",
      " [ 0.59367454  1.88587     1.419647    0.33041742 -0.6096125  -0.5809007\n",
      "  -1.8819841   0.9125827  -0.78063124 -1.4896778   0.6555146   0.8121288\n",
      "   0.37038496  1.6859787  -3.0017133  -2.543954  ]\n",
      " [ 3.1952624  -0.4948602   1.755326    2.1172898  -0.13145918  0.6117851\n",
      "   0.8252412  -0.83210164 -2.356029    1.1727926  -0.57581884  0.8280243\n",
      "   0.6832328  -2.7742715  -1.5130864  -1.0616964 ]]\n",
      "reconstituted: [[1.21429521e-05 3.68534056e-05 1.16213749e-04 1.56786977e-04\n",
      "  1.28794112e-04 3.77826975e-04 1.33374866e-04 3.76376323e-03\n",
      "  3.03900661e-03 6.46047993e-03 6.33064378e-03 1.02048973e-02\n",
      "  8.06858670e-03 6.40473217e-02 5.01521565e-02 8.12866539e-02\n",
      "  1.12737283e-01 2.47304048e-02 3.17946970e-02 9.03499722e-02\n",
      "  1.14339066e-03 2.39250599e-03 2.70266528e-03 1.59065258e-02\n",
      "  9.01083462e-03 3.10931187e-02 3.37470211e-02 9.20621678e-03\n",
      "  7.36819999e-03 4.67046164e-02 1.57459766e-01 5.46552241e-02\n",
      "  9.61580649e-02 2.02848874e-02 8.17434490e-02]\n",
      " [4.39641881e-05 1.66294674e-04 6.74210081e-04 6.02529268e-04\n",
      "  5.37321204e-04 2.35725520e-03 1.13948784e-03 1.51972827e-02\n",
      "  1.11022377e-02 2.11650692e-02 4.98179439e-03 1.03497468e-02\n",
      "  7.20296474e-03 4.89620641e-02 2.57472973e-02 3.20402123e-02\n",
      "  5.98860383e-02 1.32321073e-02 1.92552414e-02 6.10533692e-02\n",
      "  9.40190163e-04 4.35973192e-03 2.44202837e-03 2.76341345e-02\n",
      "  6.75446168e-03 9.11084283e-03 1.48186028e-01 3.63489874e-02\n",
      "  3.11243758e-02 1.34306461e-01 1.40060499e-01 5.89571632e-02\n",
      "  1.00232445e-01 1.33904116e-02 2.65090056e-02]\n",
      " [8.69377004e-07 3.83365186e-05 1.35253510e-03 1.20407995e-03\n",
      "  6.67615168e-05 2.52750469e-03 1.82459108e-03 2.62649381e-03\n",
      "  2.17984873e-03 6.70667097e-04 8.86453257e-04 1.27440486e-02\n",
      "  9.59519576e-03 1.35327345e-02 7.58158136e-03 2.31845817e-03\n",
      "  2.79743411e-02 1.83115322e-02 3.52735166e-03 2.99272523e-03\n",
      "  2.46651325e-04 1.75766274e-02 1.64840929e-02 2.35404894e-02\n",
      "  1.68516356e-02 1.76235521e-03 4.45350975e-01 3.03080082e-01\n",
      "  2.04616189e-02 2.01614071e-02 2.02919971e-02 1.31199937e-02\n",
      "  2.96998490e-03 5.04265763e-02 4.06321418e-03]\n",
      " [1.08909908e-05 2.43943054e-04 1.37723866e-03 6.82595558e-03\n",
      "  2.25237804e-04 6.78058714e-03 2.24196743e-02 4.24564909e-03\n",
      "  4.60452437e-02 2.39990540e-02 1.61367381e-04 1.03111460e-03\n",
      "  1.00658629e-02 1.54242245e-03 1.05137952e-01 1.30798578e-01\n",
      "  1.55061006e-03 3.84911597e-02 2.57142987e-02 5.25074415e-02\n",
      "  1.65496152e-04 1.27835316e-03 1.89168025e-02 1.80020614e-03\n",
      "  4.94726188e-02 2.70150788e-02 6.34764042e-03 2.93006692e-02\n",
      "  3.03290542e-02 1.39377400e-01 5.33214677e-03 1.74985677e-02\n",
      "  2.60355845e-02 1.75618790e-02 2.30794087e-01]\n",
      " [1.55818532e-04 1.72124757e-03 5.58222830e-03 6.90696714e-03\n",
      "  2.89620645e-03 2.03840733e-02 2.51357183e-02 1.83639564e-02\n",
      "  2.14493237e-02 5.93860587e-03 3.56938457e-03 1.09289493e-02\n",
      "  6.81577064e-03 1.32373134e-02 7.03079766e-03 4.34231991e-03\n",
      "  1.44613432e-02 1.94656514e-02 1.07403239e-02 1.74142327e-02\n",
      "  2.56694504e-03 2.50485558e-02 2.54933331e-02 5.08247130e-02\n",
      "  3.03143635e-02 5.64828701e-03 1.44561231e-01 7.10223690e-02\n",
      "  2.17981245e-02 1.65353522e-01 4.41551916e-02 5.53673320e-02\n",
      "  3.15784737e-02 3.74449678e-02 3.04328185e-02]\n",
      " [2.36263604e-05 6.41499879e-04 6.55963598e-03 1.18933514e-01\n",
      "  1.00626348e-04 1.19320489e-03 4.96884361e-02 1.34659145e-04\n",
      "  2.52405778e-02 1.08077740e-02 1.12164023e-04 2.20121886e-03\n",
      "  3.37617807e-02 3.84166662e-04 5.92276081e-02 3.34977061e-02\n",
      "  1.08672742e-04 3.54560390e-02 2.94294208e-02 1.19644124e-02\n",
      "  7.42638076e-05 1.73112913e-03 3.07074469e-02 1.61926102e-04\n",
      "  1.00255897e-02 7.90677965e-03 3.02252127e-04 2.58010216e-02\n",
      "  1.70972776e-02 2.59341039e-02 8.51201545e-03 7.53520802e-02\n",
      "  1.52917549e-01 2.42402777e-01 3.52950282e-02]\n",
      " [1.19335655e-05 1.07918488e-04 3.42348765e-04 4.30444302e-03\n",
      "  2.86380789e-04 2.74467585e-03 2.53955591e-02 5.74094709e-03\n",
      "  6.76105917e-02 5.90057261e-02 1.10537578e-04 3.04491929e-04\n",
      "  1.84346817e-03 1.06885342e-03 1.23641873e-02 5.38033843e-02\n",
      "  1.81724585e-03 4.29941565e-02 1.15637541e-01 7.74370506e-02\n",
      "  3.36254489e-05 1.59009360e-04 1.52279926e-03 9.80759272e-04\n",
      "  1.92573145e-02 7.46572539e-02 4.89586918e-03 2.04674657e-02\n",
      "  4.24466990e-02 7.32434168e-02 1.00109458e-03 1.68262310e-02\n",
      "  5.92170246e-02 8.84820819e-02 1.11353226e-01]\n",
      " [3.28180380e-04 1.17757730e-03 6.19323365e-03 2.84155831e-03\n",
      "  5.91087015e-03 3.53519320e-02 1.30925896e-02 9.65550318e-02\n",
      "  3.25873159e-02 1.97475199e-02 5.49050048e-03 1.42102167e-02\n",
      "  4.14738571e-03 5.18554561e-02 9.89084691e-03 7.60363089e-03\n",
      "  6.56404197e-02 2.23295782e-02 2.90633682e-02 2.49908548e-02\n",
      "  1.15930871e-03 6.97747013e-03 1.31605507e-03 4.46592085e-02\n",
      "  5.44602936e-03 7.54904514e-03 2.46644258e-01 2.65845750e-02\n",
      "  1.97962597e-02 4.50884923e-02 4.04977128e-02 1.59286950e-02\n",
      "  3.96052822e-02 8.59784931e-02 2.43687946e-02]\n",
      " [8.18706880e-08 9.32927378e-06 9.12718941e-04 1.12298541e-02\n",
      "  6.08701885e-06 5.05170203e-04 3.15757492e-03 1.83520853e-04\n",
      "  2.01880909e-03 2.10977742e-03 4.82653522e-05 2.69008381e-03\n",
      "  1.91961080e-02 7.81688199e-04 4.84746555e-03 4.26611910e-03\n",
      "  1.25767747e-02 6.39516860e-02 1.30206132e-02 4.90796426e-03\n",
      "  5.62303067e-05 9.41976346e-03 1.03029013e-01 5.58162946e-03\n",
      "  2.28336081e-02 2.87400503e-02 7.49456435e-02 3.84976119e-01\n",
      "  1.34085849e-01 6.31476846e-03 5.38808014e-03 2.33999193e-02\n",
      "  1.60426460e-02 4.68242541e-02 3.72840161e-03]\n",
      " [3.05951994e-06 9.28646041e-05 2.87479046e-03 5.44992089e-03\n",
      "  9.08503134e-04 2.66305823e-02 3.36929634e-02 5.30949347e-02\n",
      "  4.34593260e-02 3.63239758e-02 2.89023761e-03 3.86147536e-02\n",
      "  4.14919220e-02 9.47459340e-02 9.10341144e-02 6.83870912e-02\n",
      "  2.98527721e-02 5.92791401e-02 3.85271646e-02 9.77868494e-03\n",
      "  1.87484853e-04 6.44348515e-03 1.08518163e-02 2.10634358e-02\n",
      "  2.48529464e-02 2.36443132e-02 4.77201492e-02 2.09317300e-02\n",
      "  5.27971871e-02 7.02512404e-03 1.31833786e-02 1.37115456e-02\n",
      "  1.64042711e-02 2.09425054e-02 2.73368973e-02]]\n"
     ]
    }
   ],
   "source": [
    "n_to_show = 10\n",
    "example_idx = np.random.choice(range(len(x_test)), n_to_show)\n",
    "example_images = x_test[example_idx]\n",
    "example_test   = y_test[example_idx]\n",
    "\n",
    "z_points = AE.encoder.predict(example_images)\n",
    "print('z_points: ', z_points)\n",
    "reconst_images = AE.decoder.predict(z_points)\n",
    "print('reconstituted: {}'.format(reconst_images))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-0.8850612  -1.4595169   0.3192515   3.057255    0.26186588 -0.71518177\n",
      " -1.1694394   1.1918801  -3.696774    0.20555446 -2.353115    0.32696095\n",
      " -2.023741   -1.2376521  -1.1473072   0.49997568]\n"
     ]
    }
   ],
   "source": [
    "print(z_points[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[4.39641881e-05 1.66294674e-04 6.74210081e-04 6.02529268e-04\n",
      " 5.37321204e-04 2.35725520e-03 1.13948784e-03 1.51972827e-02\n",
      " 1.11022377e-02 2.11650692e-02 4.98179439e-03 1.03497468e-02\n",
      " 7.20296474e-03 4.89620641e-02 2.57472973e-02 3.20402123e-02\n",
      " 5.98860383e-02 1.32321073e-02 1.92552414e-02 6.10533692e-02\n",
      " 9.40190163e-04 4.35973192e-03 2.44202837e-03 2.76341345e-02\n",
      " 6.75446168e-03 9.11084283e-03 1.48186028e-01 3.63489874e-02\n",
      " 3.11243758e-02 1.34306461e-01 1.40060499e-01 5.89571632e-02\n",
      " 1.00232445e-01 1.33904116e-02 2.65090056e-02]\n"
     ]
    }
   ],
   "source": [
    "print(reconst_images[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0760532535350649\n"
     ]
    }
   ],
   "source": [
    "print(sum(reconst_images[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'AE Generated Outcome From Experimental Inputs')"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pos = np.arange(len(reconst_images[1]))\n",
    "\n",
    "objects = list(range(len(reconst_images[1])))\n",
    "\n",
    "plt.bar(y_pos, reconst_images[1], align='center', alpha=0.5)\n",
    "plt.xticks(y_pos, objects)\n",
    "plt.ylabel('Probability')\n",
    "plt.title('AE Generated Outcome From Experimental Inputs')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Brute Force Calculated Results')"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pos = np.arange(len(reconst_images[1]))\n",
    "\n",
    "objects = list(range(len(reconst_images[1])))\n",
    "\n",
    "plt.bar(y_pos, example_test[1], align='center', alpha=0.5)\n",
    "plt.xticks(y_pos, objects)\n",
    "plt.ylabel('Probability')\n",
    "plt.title('Brute Force Calculated Results')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}