altencoder-nobuyuki.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dataset members have the following lengths: {18438} (expected 18438)\n",
      "Has 0 corrupt value(s), 1240 values, 1240 keys\n",
      "dataset size: (1240, 18438)\n",
      "partitioning as train = (800, 18438), test = (440, 18438)\n"
     ]
    }
   ],
   "source": [
    "import json\n",
    "import urllib\n",
    "import zipfile\n",
    "import io\n",
    "import numpy as np\n",
    "\n",
    "# Load dataset\n",
    "NB_DATASET_URL = 'https://github.com/SeijiEmery/shape-net-data/raw/master/car-vertex-data/nobuyuki-data.json.zip'\n",
    "with urllib.request.urlopen(NB_DATASET_URL) as f:\n",
    "    with zipfile.ZipFile(io.BytesIO(f.read()), 'r') as zf:\n",
    "        with zf.open('nobuyuki-data.json', 'r') as f:\n",
    "            sw_dataset = json.loads(f.read())\n",
    "\n",
    "sw_dimensions = set(map(len, sw_dataset.values()))\n",
    "print(\"dataset members have the following lengths: %s (expected 18438)\"%(sw_dimensions,))\n",
    "\n",
    "sw_keys = [ key for key in sw_dataset.keys() if len(sw_dataset[key]) == 18438 ]\n",
    "non_corrupt_values = [ x for x in sw_dataset.values() if len(x) == 18438 ]\n",
    "corrupt_values = [ x for x in sw_dataset.values() if len(x) != 18438 ]\n",
    "print(\"Has %s corrupt value(s), %s values, %s keys\"%(\n",
    "    len(corrupt_values), len(non_corrupt_values), len(sw_keys)))\n",
    "\n",
    "sw_data = np.array(non_corrupt_values)\n",
    "print(\"dataset size: %s\"%(sw_data.shape,))\n",
    "\n",
    "x_train, x_test = np.split(sw_data, [800], 0)\n",
    "print(\"partitioning as train = %s, test = %s\"%(x_train.shape, x_test.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_25 (Dense)             (None, 128)               2360192   \n",
      "_________________________________________________________________\n",
      "activation_25 (Activation)   (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "dense_26 (Dense)             (None, 10)                1290      \n",
      "_________________________________________________________________\n",
      "activation_26 (Activation)   (None, 10)                0         \n",
      "_________________________________________________________________\n",
      "dense_27 (Dense)             (None, 128)               1408      \n",
      "_________________________________________________________________\n",
      "activation_27 (Activation)   (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "dense_28 (Dense)             (None, 18438)             2378502   \n",
      "_________________________________________________________________\n",
      "activation_28 (Activation)   (None, 18438)             0         \n",
      "=================================================================\n",
      "Total params: 4,741,392\n",
      "Trainable params: 4,741,392\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Input, Dense, Activation\n",
    "from keras.models import Model, load_model\n",
    "import os\n",
    "\n",
    "INPUT_DIM = 18438\n",
    "HIDDEN_DIM = 128\n",
    "LATENT_DIM = 10\n",
    "\n",
    "model = Sequential([\n",
    "    Dense(HIDDEN_DIM, input_shape=(INPUT_DIM,)),\n",
    "    Activation('relu'),\n",
    "    Dense(LATENT_DIM),\n",
    "    Activation('relu'),\n",
    "    Dense(HIDDEN_DIM),\n",
    "    Activation('relu'),\n",
    "    Dense(INPUT_DIM),\n",
    "    Activation('linear')\n",
    "])\n",
    "trained_epochs = 0\n",
    "model.compile(optimizer='adam', loss='mse')\n",
    "model.summary()\n",
    "\n",
    "def train (epochs, batch_size=50):\n",
    "    global model, trained_epochs\n",
    "    print(\"Training: epoch %s -> %s\"%(trained_epochs, epochs + trained_epochs))\n",
    "    model.fit(x_train, x_train,\n",
    "             epochs=epochs,\n",
    "             batch_size=batch_size)\n",
    "    trained_epochs += epochs\n",
    "    if not os.path.exists('altmodel'):\n",
    "        os.makedirs('altmodel')\n",
    "    path = os.path.join('altmodel', 'altmodel-epoch-%s.h5'%(trained_epochs,))\n",
    "    print(\"Saving snapshot as %s\"%path)\n",
    "    model.save(path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training: epoch 500 -> 600\n",
      "Epoch 1/100\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0027\n",
      "Epoch 2/100\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0027\n",
      "Epoch 3/100\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0026\n",
      "Epoch 4/100\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0027\n",
      "Epoch 5/100\n",
      "800/800 [==============================] - 2s 2ms/step - loss: 0.0028\n",
      "Epoch 6/100\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0027\n",
      "Epoch 7/100\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0026\n",
      "Epoch 8/100\n",
      "800/800 [==============================] - 2s 2ms/step - loss: 0.0026\n",
      "Epoch 9/100\n",
      "800/800 [==============================] - 2s 3ms/step - loss: 0.0026\n",
      "Epoch 10/100\n",
      "800/800 [==============================] - 3s 4ms/step - loss: 0.0028\n",
      "Epoch 11/100\n",
      "800/800 [==============================] - 3s 3ms/step - loss: 0.0032\n",
      "Epoch 12/100\n",
      "800/800 [==============================] - 3s 3ms/step - loss: 0.0027\n",
      "Epoch 13/100\n",
      "800/800 [==============================] - 3s 4ms/step - loss: 0.0027\n",
      "Epoch 14/100\n",
      "800/800 [==============================] - 3s 4ms/step - loss: 0.0026\n",
      "Epoch 15/100\n",
      "800/800 [==============================] - 3s 3ms/step - loss: 0.0027\n",
      "Epoch 16/100\n",
      "800/800 [==============================] - 3s 3ms/step - loss: 0.0026\n",
      "Epoch 17/100\n",
      "800/800 [==============================] - 3s 3ms/step - loss: 0.0026\n",
      "Epoch 18/100\n",
      "800/800 [==============================] - 3s 4ms/step - loss: 0.0026\n",
      "Epoch 19/100\n",
      "800/800 [==============================] - 3s 3ms/step - loss: 0.0027\n",
      "Epoch 20/100\n",
      "800/800 [==============================] - 3s 4ms/step - loss: 0.0026\n",
      "Epoch 21/100\n",
      "800/800 [==============================] - 3s 3ms/step - loss: 0.0028\n",
      "Epoch 22/100\n",
      "800/800 [==============================] - 3s 3ms/step - loss: 0.0029\n",
      "Epoch 23/100\n",
      "800/800 [==============================] - 3s 3ms/step - loss: 0.0028\n",
      "Epoch 24/100\n",
      "800/800 [==============================] - 3s 3ms/step - loss: 0.0029\n",
      "Epoch 25/100\n",
      "800/800 [==============================] - 3s 3ms/step - loss: 0.0028\n",
      "Epoch 26/100\n",
      "800/800 [==============================] - 3s 3ms/step - loss: 0.0027\n",
      "Epoch 27/100\n",
      "800/800 [==============================] - 3s 4ms/step - loss: 0.0028\n",
      "Epoch 28/100\n",
      "800/800 [==============================] - 3s 4ms/step - loss: 0.0026\n",
      "Epoch 29/100\n",
      "800/800 [==============================] - 3s 4ms/step - loss: 0.0026\n",
      "Epoch 30/100\n",
      "800/800 [==============================] - 3s 3ms/step - loss: 0.0026\n",
      "Epoch 31/100\n",
      "800/800 [==============================] - 3s 4ms/step - loss: 0.0026\n",
      "Epoch 32/100\n",
      "800/800 [==============================] - 3s 4ms/step - loss: 0.0026\n",
      "Epoch 33/100\n",
      "800/800 [==============================] - 3s 4ms/step - loss: 0.0026\n",
      "Epoch 34/100\n",
      "800/800 [==============================] - 3s 4ms/step - loss: 0.0026\n",
      "Epoch 35/100\n",
      "800/800 [==============================] - 3s 3ms/step - loss: 0.0026\n",
      "Epoch 36/100\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0027\n",
      "Epoch 37/100\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0027\n",
      "Epoch 38/100\n",
      "800/800 [==============================] - 2s 2ms/step - loss: 0.0029\n",
      "Epoch 39/100\n",
      "800/800 [==============================] - 2s 2ms/step - loss: 0.0028\n",
      "Epoch 40/100\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0028\n",
      "Epoch 41/100\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0028\n",
      "Epoch 42/100\n",
      "800/800 [==============================] - 2s 3ms/step - loss: 0.0027\n",
      "Epoch 43/100\n",
      "800/800 [==============================] - 3s 3ms/step - loss: 0.0026\n",
      "Epoch 44/100\n",
      "800/800 [==============================] - 3s 3ms/step - loss: 0.0026\n",
      "Epoch 45/100\n",
      "800/800 [==============================] - 2s 3ms/step - loss: 0.0026\n",
      "Epoch 46/100\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0026\n",
      "Epoch 47/100\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0026\n",
      "Epoch 48/100\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0026\n",
      "Epoch 49/100\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0026\n",
      "Epoch 50/100\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0026\n",
      "Epoch 51/100\n",
      "800/800 [==============================] - 2s 3ms/step - loss: 0.0026\n",
      "Epoch 52/100\n",
      "800/800 [==============================] - 3s 4ms/step - loss: 0.0027\n",
      "Epoch 53/100\n",
      "800/800 [==============================] - 2s 3ms/step - loss: 0.0026\n",
      "Epoch 54/100\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0026\n",
      "Epoch 55/100\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0026\n",
      "Epoch 56/100\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0028\n",
      "Epoch 57/100\n",
      "800/800 [==============================] - 2s 2ms/step - loss: 0.0026\n",
      "Epoch 58/100\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0026\n",
      "Epoch 59/100\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0028\n",
      "Epoch 60/100\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0027\n",
      "Epoch 61/100\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0027\n",
      "Epoch 62/100\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0026\n",
      "Epoch 63/100\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0027\n",
      "Epoch 64/100\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0028\n",
      "Epoch 65/100\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0028\n",
      "Epoch 66/100\n",
      "800/800 [==============================] - 2s 2ms/step - loss: 0.0027\n",
      "Epoch 67/100\n",
      "800/800 [==============================] - 2s 2ms/step - loss: 0.0027\n",
      "Epoch 68/100\n",
      "800/800 [==============================] - 2s 2ms/step - loss: 0.0026\n",
      "Epoch 69/100\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0026\n",
      "Epoch 70/100\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0026\n",
      "Epoch 71/100\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0026\n",
      "Epoch 72/100\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0026\n",
      "Epoch 73/100\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0027\n",
      "Epoch 74/100\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0026\n",
      "Epoch 75/100\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0026\n",
      "Epoch 76/100\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0026\n",
      "Epoch 77/100\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0026\n",
      "Epoch 78/100\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0027\n",
      "Epoch 79/100\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0027\n",
      "Epoch 80/100\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0027\n",
      "Epoch 81/100\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0028\n",
      "Epoch 82/100\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0029\n",
      "Epoch 83/100\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0027\n",
      "Epoch 84/100\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0027\n",
      "Epoch 85/100\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0026\n",
      "Epoch 86/100\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0026\n",
      "Epoch 87/100\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0027\n",
      "Epoch 88/100\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0026\n",
      "Epoch 89/100\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0026\n",
      "Epoch 90/100\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0025\n",
      "Epoch 91/100\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0026\n",
      "Epoch 92/100\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0026\n",
      "Epoch 93/100\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0026\n",
      "Epoch 94/100\n",
      "800/800 [==============================] - 2s 2ms/step - loss: 0.0026\n",
      "Epoch 95/100\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0026\n",
      "Epoch 96/100\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0028\n",
      "Epoch 97/100\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0026\n",
      "Epoch 98/100\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0026\n",
      "Epoch 99/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0027\n",
      "Epoch 100/100\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0027\n",
      "Saving snapshot as altmodel/altmodel-epoch-600.h5\n",
      "Loading altmodel/altmodel-epoch-600.h5\n",
      "predicting for epoch 600\n",
      "generating write-obj jobs for epoch 600\n",
      "dispatching write-obj jobs for epoch 600\n",
      "writing altencoder-objs/600/input/train-b8c8758f8ee058182a9eb0f146e94477-input.obj\n",
      "writing altencoder-objs/600/output/train-b8c8758f8ee058182a9eb0f146e94477-output.obj\n",
      "writing altencoder-objs/600/input/test-a52f704da0d3c947ca92a4cdad802b45-input.obj\n",
      "writing altencoder-objs/600/output/test-a52f704da0d3c947ca92a4cdad802b45-output.obj\n",
      "writing altencoder-objs/600/input/train-9b0d7aac4500546911718baa6d4afc0b-input.obj\n",
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      "writing altencoder-objs/600/input/test-f61888bca4392106bc790478b4a84bf2-input.obj\n",
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      "writing altencoder-objs/600/output/train-180d1e3463c29355bda72093f9b5aa73-output.obj\n",
      "writing altencoder-objs/600/input/test-b0a659083ec6e936ca92a4cdad802b45-input.obj\n",
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      "writing altencoder-objs/600/input/train-d849e0fee026c98aeece2324fbbe339-input.obj\n",
      "writing altencoder-objs/600/output/train-d849e0fee026c98aeece2324fbbe339-output.obj\n",
      "writing altencoder-objs/600/input/test-862a345b6328d641ef3ecc8551d2c1fa-input.obj\n",
      "writing altencoder-objs/600/output/test-862a345b6328d641ef3ecc8551d2c1fa-output.obj\n",
      "writing altencoder-objs/600/input/train-c61ec8875746ab78227375499b768057-input.obj\n",
      "writing altencoder-objs/600/output/train-c61ec8875746ab78227375499b768057-output.obj\n",
      "writing altencoder-objs/600/input/test-12cf05b9f5f2d2c195c8d3a7db4dbe65-input.obj\n",
      "writing altencoder-objs/600/output/test-12cf05b9f5f2d2c195c8d3a7db4dbe65-output.obj\n",
      "writing altencoder-objs/600/input/train-83f26e6b8298c3a0a134736201a79843-input.obj\n",
      "writing altencoder-objs/600/output/train-83f26e6b8298c3a0a134736201a79843-output.obj\n",
      "writing altencoder-objs/600/input/test-9c4a3879c71df693af0f25977186b501-input.obj\n",
      "writing altencoder-objs/600/output/test-9c4a3879c71df693af0f25977186b501-output.obj\n",
      "done processing models for epoch 600\n",
      "Training: epoch 600 -> 700\n",
      "Epoch 1/100\n",
      "800/800 [==============================] - 2s 2ms/step - loss: 0.0026\n",
      "Epoch 2/100\n",
      "800/800 [==============================] - 2s 2ms/step - loss: 0.0026\n",
      "Epoch 3/100\n",
      "800/800 [==============================] - 3s 3ms/step - loss: 0.0025\n",
      "Epoch 4/100\n",
      "800/800 [==============================] - 3s 4ms/step - loss: 0.0026\n",
      "Epoch 5/100\n",
      "800/800 [==============================] - 3s 3ms/step - loss: 0.0025\n",
      "Epoch 6/100\n",
      "800/800 [==============================] - 3s 3ms/step - loss: 0.0025\n",
      "Epoch 7/100\n",
      "800/800 [==============================] - 3s 3ms/step - loss: 0.0026\n",
      "Epoch 8/100\n",
      "800/800 [==============================] - 3s 3ms/step - loss: 0.0025\n",
      "Epoch 9/100\n",
      "800/800 [==============================] - 3s 3ms/step - loss: 0.0027\n",
      "Epoch 10/100\n",
      "800/800 [==============================] - 3s 4ms/step - loss: 0.0026\n",
      "Epoch 11/100\n",
      "800/800 [==============================] - 3s 4ms/step - loss: 0.0028\n",
      "Epoch 12/100\n",
      "800/800 [==============================] - 3s 4ms/step - loss: 0.0027\n",
      "Epoch 13/100\n",
      "800/800 [==============================] - 3s 3ms/step - loss: 0.0026\n",
      "Epoch 14/100\n",
      "800/800 [==============================] - 3s 3ms/step - loss: 0.0026\n",
      "Epoch 15/100\n",
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     ]
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      "Saving snapshot as altmodel/altmodel-epoch-700.h5\n",
      "Loading altmodel/altmodel-epoch-700.h5\n",
      "predicting for epoch 700\n",
      "generating write-obj jobs for epoch 700\n",
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      "done processing models for epoch 700\n",
      "Training: epoch 700 -> 800\n",
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     ]
    },
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      "Epoch 100/100\n",
      "800/800 [==============================] - 2s 3ms/step - loss: 0.0026\n",
      "Saving snapshot as altmodel/altmodel-epoch-800.h5\n",
      "Loading altmodel/altmodel-epoch-800.h5\n",
      "predicting for epoch 800\n",
      "generating write-obj jobs for epoch 800\n",
      "dispatching write-obj jobs for epoch 800\n",
      "writing altencoder-objs/800/input/train-b8c8758f8ee058182a9eb0f146e94477-input.obj\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "writing altencoder-objs/800/input/train-ba1b8956b1d84856369912a1ddf80ef7-input.obj\n",
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      "writing altencoder-objs/800/output/test-9c4a3879c71df693af0f25977186b501-output.obj\n",
      "done processing models for epoch 800\n",
      "Training: epoch 800 -> 900\n",
      "Epoch 1/100\n",
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      "Epoch 70/100\n"
     ]
    },
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     "text": [
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      "Epoch 71/100\n",
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      "Saving snapshot as altmodel/altmodel-epoch-900.h5\n",
      "Loading altmodel/altmodel-epoch-900.h5\n",
      "predicting for epoch 900\n",
      "generating write-obj jobs for epoch 900\n",
      "dispatching write-obj jobs for epoch 900\n",
      "writing altencoder-objs/900/input/train-b8c8758f8ee058182a9eb0f146e94477-input.obj\n",
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      "writing altencoder-objs/900/input/test-b0a659083ec6e936ca92a4cdad802b45-input.obj\n",
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      "writing altencoder-objs/900/input/train-d849e0fee026c98aeece2324fbbe339-input.obj\n",
      "writing altencoder-objs/900/output/train-d849e0fee026c98aeece2324fbbe339-output.obj\n",
      "writing altencoder-objs/900/input/test-862a345b6328d641ef3ecc8551d2c1fa-input.obj\n",
      "writing altencoder-objs/900/output/test-862a345b6328d641ef3ecc8551d2c1fa-output.obj\n",
      "writing altencoder-objs/900/input/train-c61ec8875746ab78227375499b768057-input.obj\n",
      "writing altencoder-objs/900/output/train-c61ec8875746ab78227375499b768057-output.obj\n",
      "writing altencoder-objs/900/input/test-12cf05b9f5f2d2c195c8d3a7db4dbe65-input.obj\n",
      "writing altencoder-objs/900/output/test-12cf05b9f5f2d2c195c8d3a7db4dbe65-output.obj\n",
      "writing altencoder-objs/900/input/train-83f26e6b8298c3a0a134736201a79843-input.obj\n",
      "writing altencoder-objs/900/output/train-83f26e6b8298c3a0a134736201a79843-output.obj\n",
      "writing altencoder-objs/900/input/test-9c4a3879c71df693af0f25977186b501-input.obj\n",
      "writing altencoder-objs/900/output/test-9c4a3879c71df693af0f25977186b501-output.obj\n",
      "done processing models for epoch 900\n",
      "Training: epoch 900 -> 1000\n",
      "Epoch 1/100\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0026\n",
      "Epoch 2/100\n",
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      "Epoch 3/100\n",
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      "Epoch 8/100\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0026\n",
      "Epoch 9/100\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0026\n",
      "Epoch 10/100\n",
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      "Epoch 11/100\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0025\n",
      "Epoch 12/100\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0025\n",
      "Epoch 13/100\n",
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      "Epoch 14/100\n",
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      "Epoch 16/100\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0027\n",
      "Epoch 17/100\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0028\n",
      "Epoch 18/100\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0027\n",
      "Epoch 19/100\n",
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      "Epoch 20/100\n",
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      "Epoch 22/100\n",
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      "Epoch 23/100\n",
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      "Epoch 24/100\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0025\n",
      "Epoch 25/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0025\n",
      "Epoch 26/100\n",
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      "Saving snapshot as altmodel/altmodel-epoch-1000.h5\n",
      "Loading altmodel/altmodel-epoch-1000.h5\n",
      "predicting for epoch 1000\n",
      "generating write-obj jobs for epoch 1000\n",
      "dispatching write-obj jobs for epoch 1000\n",
      "writing altencoder-objs/1000/input/train-b8c8758f8ee058182a9eb0f146e94477-input.obj\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "writing altencoder-objs/1000/input/train-180d1e3463c29355bda72093f9b5aa73-input.obj\n",
      "writing altencoder-objs/1000/output/train-180d1e3463c29355bda72093f9b5aa73-output.obj\n",
      "writing altencoder-objs/1000/input/test-b0a659083ec6e936ca92a4cdad802b45-input.obj\n",
      "writing altencoder-objs/1000/output/test-b0a659083ec6e936ca92a4cdad802b45-output.obj\n",
      "writing altencoder-objs/1000/input/train-d849e0fee026c98aeece2324fbbe339-input.obj\n",
      "writing altencoder-objs/1000/output/train-d849e0fee026c98aeece2324fbbe339-output.obj\n",
      "writing altencoder-objs/1000/input/test-862a345b6328d641ef3ecc8551d2c1fa-input.obj\n",
      "writing altencoder-objs/1000/output/test-862a345b6328d641ef3ecc8551d2c1fa-output.obj\n",
      "writing altencoder-objs/1000/input/train-c61ec8875746ab78227375499b768057-input.obj\n",
      "writing altencoder-objs/1000/output/train-c61ec8875746ab78227375499b768057-output.obj\n",
      "writing altencoder-objs/1000/input/test-12cf05b9f5f2d2c195c8d3a7db4dbe65-input.obj\n",
      "writing altencoder-objs/1000/output/test-12cf05b9f5f2d2c195c8d3a7db4dbe65-output.obj\n",
      "writing altencoder-objs/1000/input/train-83f26e6b8298c3a0a134736201a79843-input.obj\n",
      "writing altencoder-objs/1000/output/train-83f26e6b8298c3a0a134736201a79843-output.obj\n",
      "writing altencoder-objs/1000/input/test-9c4a3879c71df693af0f25977186b501-input.obj\n",
      "writing altencoder-objs/1000/output/test-9c4a3879c71df693af0f25977186b501-output.obj\n",
      "done processing models for epoch 1000\n",
      "Training: epoch 1000 -> 1100\n",
      "Epoch 1/100\n",
      "800/800 [==============================] - 2s 2ms/step - loss: 0.0025\n",
      "Epoch 2/100\n",
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      "Epoch 32/100\n",
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      "Epoch 36/100\n",
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      "Epoch 37/100\n",
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      "Epoch 38/100\n",
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      "Epoch 39/100\n",
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      "Epoch 40/100\n",
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      "Epoch 42/100\n",
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      "Epoch 43/100\n",
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      "Epoch 44/100\n",
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      "Epoch 45/100\n",
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      "Epoch 82/100\n"
     ]
    },
    {
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     "text": [
      "800/800 [==============================] - 2s 2ms/step - loss: 0.0024\n",
      "Epoch 83/100\n",
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      "Saving snapshot as altmodel/altmodel-epoch-1100.h5\n",
      "Loading altmodel/altmodel-epoch-1100.h5\n",
      "predicting for epoch 1100\n",
      "generating write-obj jobs for epoch 1100\n",
      "dispatching write-obj jobs for epoch 1100\n",
      "writing altencoder-objs/1100/input/train-b8c8758f8ee058182a9eb0f146e94477-input.obj\n",
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      "writing altencoder-objs/1100/input/train-83f26e6b8298c3a0a134736201a79843-input.obj\n",
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      "writing altencoder-objs/1100/output/test-9c4a3879c71df693af0f25977186b501-output.obj\n",
      "done processing models for epoch 1100\n",
      "Training: epoch 1100 -> 1200\n",
      "Epoch 1/100\n",
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      "Epoch 37/100\n"
     ]
    },
    {
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     "text": [
      "800/800 [==============================] - 2s 2ms/step - loss: 0.0025\n",
      "Epoch 38/100\n",
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      "Epoch 100/100\n",
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      "Saving snapshot as altmodel/altmodel-epoch-1200.h5\n",
      "Loading altmodel/altmodel-epoch-1200.h5\n",
      "predicting for epoch 1200\n",
      "generating write-obj jobs for epoch 1200\n",
      "dispatching write-obj jobs for epoch 1200\n",
      "writing altencoder-objs/1200/input/train-b8c8758f8ee058182a9eb0f146e94477-input.obj\n",
      "writing altencoder-objs/1200/output/train-b8c8758f8ee058182a9eb0f146e94477-output.obj\n",
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      "writing altencoder-objs/1200/input/test-12cf05b9f5f2d2c195c8d3a7db4dbe65-input.obj\n",
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      "writing altencoder-objs/1200/input/train-83f26e6b8298c3a0a134736201a79843-input.obj\n",
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      "writing altencoder-objs/1200/input/test-9c4a3879c71df693af0f25977186b501-input.obj\n",
      "writing altencoder-objs/1200/output/test-9c4a3879c71df693af0f25977186b501-output.obj\n",
      "done processing models for epoch 1200\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training: epoch 1200 -> 1300\n",
      "Epoch 1/100\n",
      "800/800 [==============================] - 2s 2ms/step - loss: 0.0024\n",
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      "Epoch 8/100\n",
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      "Epoch 13/100\n",
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      "Epoch 14/100\n",
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      "Epoch 23/100\n",
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      "Epoch 33/100\n",
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      "Epoch 34/100\n",
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      "Epoch 35/100\n",
      "800/800 [==============================] - 2s 3ms/step - loss: 0.0024\n",
      "Epoch 36/100\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0025\n",
      "Epoch 37/100\n",
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      "Epoch 40/100\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0024A: 0s - loss: 0.\n",
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      "Epoch 99/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0025\n",
      "Epoch 100/100\n",
      "800/800 [==============================] - 1s 1ms/step - loss: 0.0024\n",
      "Saving snapshot as altmodel/altmodel-epoch-1300.h5\n",
      "Loading altmodel/altmodel-epoch-1300.h5\n",
      "predicting for epoch 1300\n",
      "generating write-obj jobs for epoch 1300\n",
      "dispatching write-obj jobs for epoch 1300\n",
      "writing altencoder-objs/1300/input/train-b8c8758f8ee058182a9eb0f146e94477-input.obj\n",
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      "writing altencoder-objs/1300/input/train-83f26e6b8298c3a0a134736201a79843-input.obj\n",
      "writing altencoder-objs/1300/output/train-83f26e6b8298c3a0a134736201a79843-output.obj\n",
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      "writing altencoder-objs/1300/output/test-9c4a3879c71df693af0f25977186b501-output.obj\n",
      "done processing models for epoch 1300\n",
      "Training: epoch 1300 -> 1400\n",
      "Epoch 1/100\n",
      "800/800 [==============================] - 2s 2ms/step - loss: 0.0025\n",
      "Epoch 2/100\n",
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      "Epoch 8/100\n",
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      "Epoch 10/100\n",
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      "Epoch 11/100\n",
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      "Epoch 12/100\n",
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      "Epoch 13/100\n",
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     ]
    },
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      "Saving snapshot as altmodel/altmodel-epoch-1400.h5\n",
      "Loading altmodel/altmodel-epoch-1400.h5\n",
      "predicting for epoch 1400\n",
      "generating write-obj jobs for epoch 1400\n",
      "dispatching write-obj jobs for epoch 1400\n",
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      "done processing models for epoch 1400\n",
      "Training: epoch 1400 -> 1500\n",
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     ]
    },
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      "800/800 [==============================] - 1s 2ms/step - loss: 0.0024\n",
      "Epoch 74/100\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0024\n",
      "Epoch 75/100\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0024\n",
      "Epoch 76/100\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0024\n",
      "Epoch 77/100\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0025\n",
      "Epoch 78/100\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0025\n",
      "Epoch 79/100\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0024\n",
      "Epoch 80/100\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0024\n",
      "Epoch 81/100\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0024\n",
      "Epoch 82/100\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0024\n",
      "Epoch 83/100\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0024\n",
      "Epoch 84/100\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0024\n",
      "Epoch 85/100\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0024\n",
      "Epoch 86/100\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0025\n",
      "Epoch 87/100\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0024\n",
      "Epoch 88/100\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0024\n",
      "Epoch 89/100\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0024\n",
      "Epoch 90/100\n",
      "800/800 [==============================] - 1s 1ms/step - loss: 0.0025\n",
      "Epoch 91/100\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0025\n",
      "Epoch 92/100\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0024\n",
      "Epoch 93/100\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0024\n",
      "Epoch 94/100\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0024\n",
      "Epoch 95/100\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0025\n",
      "Epoch 96/100\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0025\n",
      "Epoch 97/100\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0025\n",
      "Epoch 98/100\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0025\n",
      "Epoch 99/100\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0024\n",
      "Epoch 100/100\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0024\n",
      "Saving snapshot as altmodel/altmodel-epoch-1500.h5\n",
      "Loading altmodel/altmodel-epoch-1500.h5\n",
      "predicting for epoch 1500\n",
      "generating write-obj jobs for epoch 1500\n",
      "dispatching write-obj jobs for epoch 1500\n",
      "writing altencoder-objs/1500/input/train-b8c8758f8ee058182a9eb0f146e94477-input.obj\n",
      "writing altencoder-objs/1500/output/train-b8c8758f8ee058182a9eb0f146e94477-output.obj\n",
      "writing altencoder-objs/1500/input/test-a52f704da0d3c947ca92a4cdad802b45-input.obj\n",
      "writing altencoder-objs/1500/output/test-a52f704da0d3c947ca92a4cdad802b45-output.obj\n",
      "writing altencoder-objs/1500/input/train-9b0d7aac4500546911718baa6d4afc0b-input.obj\n",
      "writing altencoder-objs/1500/output/train-9b0d7aac4500546911718baa6d4afc0b-output.obj\n",
      "writing altencoder-objs/1500/input/test-f61888bca4392106bc790478b4a84bf2-input.obj\n",
      "writing altencoder-objs/1500/output/test-f61888bca4392106bc790478b4a84bf2-output.obj\n",
      "writing altencoder-objs/1500/input/train-86c31c04c436f5932e877e82c90c24d-input.obj\n",
      "writing altencoder-objs/1500/output/train-86c31c04c436f5932e877e82c90c24d-output.obj\n",
      "writing altencoder-objs/1500/input/test-ed2e4dafc745bdd661fd7e090d4d0d45-input.obj\n",
      "writing altencoder-objs/1500/output/test-ed2e4dafc745bdd661fd7e090d4d0d45-output.obj\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "writing altencoder-objs/1500/input/train-ba1b8956b1d84856369912a1ddf80ef7-input.obj\n",
      "writing altencoder-objs/1500/output/train-ba1b8956b1d84856369912a1ddf80ef7-output.obj\n",
      "writing altencoder-objs/1500/input/test-d2e1dc21db9b45df6436916a86a90ed7-input.obj\n",
      "writing altencoder-objs/1500/output/test-d2e1dc21db9b45df6436916a86a90ed7-output.obj\n",
      "writing altencoder-objs/1500/input/train-909422a9d1b42e1380360680c1602c7d-input.obj\n",
      "writing altencoder-objs/1500/output/train-909422a9d1b42e1380360680c1602c7d-output.obj\n",
      "writing altencoder-objs/1500/input/test-dd0b595b15a7203e185ce5d54f27f6b9-input.obj\n",
      "writing altencoder-objs/1500/output/test-dd0b595b15a7203e185ce5d54f27f6b9-output.obj\n",
      "writing altencoder-objs/1500/input/train-15cc37ac9b3b756acfd826dd468a5497-input.obj\n",
      "writing altencoder-objs/1500/output/train-15cc37ac9b3b756acfd826dd468a5497-output.obj\n",
      "writing altencoder-objs/1500/input/test-8fc3cde1054cc1aaceb4167db4d0e4de-input.obj\n",
      "writing altencoder-objs/1500/output/test-8fc3cde1054cc1aaceb4167db4d0e4de-output.obj\n",
      "writing altencoder-objs/1500/input/train-180d1e3463c29355bda72093f9b5aa73-input.obj\n",
      "writing altencoder-objs/1500/output/train-180d1e3463c29355bda72093f9b5aa73-output.obj\n",
      "writing altencoder-objs/1500/input/test-b0a659083ec6e936ca92a4cdad802b45-input.obj\n",
      "writing altencoder-objs/1500/output/test-b0a659083ec6e936ca92a4cdad802b45-output.obj\n",
      "writing altencoder-objs/1500/input/train-d849e0fee026c98aeece2324fbbe339-input.obj\n",
      "writing altencoder-objs/1500/output/train-d849e0fee026c98aeece2324fbbe339-output.obj\n",
      "writing altencoder-objs/1500/input/test-862a345b6328d641ef3ecc8551d2c1fa-input.obj\n",
      "writing altencoder-objs/1500/output/test-862a345b6328d641ef3ecc8551d2c1fa-output.obj\n",
      "writing altencoder-objs/1500/input/train-c61ec8875746ab78227375499b768057-input.obj\n",
      "writing altencoder-objs/1500/output/train-c61ec8875746ab78227375499b768057-output.obj\n",
      "writing altencoder-objs/1500/input/test-12cf05b9f5f2d2c195c8d3a7db4dbe65-input.obj\n",
      "writing altencoder-objs/1500/output/test-12cf05b9f5f2d2c195c8d3a7db4dbe65-output.obj\n",
      "writing altencoder-objs/1500/input/train-83f26e6b8298c3a0a134736201a79843-input.obj\n",
      "writing altencoder-objs/1500/output/train-83f26e6b8298c3a0a134736201a79843-output.obj\n",
      "writing altencoder-objs/1500/input/test-9c4a3879c71df693af0f25977186b501-input.obj\n",
      "writing altencoder-objs/1500/output/test-9c4a3879c71df693af0f25977186b501-output.obj\n",
      "done processing models for epoch 1500\n"
     ]
    }
   ],
   "source": [
    "for i in range(10):\n",
    "    train(100)\n",
    "    export_model_objs(trained_epochs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training: epoch 10 -> 20\n",
      "Epoch 1/10\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0058\n",
      "Epoch 2/10\n",
      "800/800 [==============================] - 2s 2ms/step - loss: 0.0040\n",
      "Epoch 3/10\n",
      "800/800 [==============================] - 2s 2ms/step - loss: 0.0038\n",
      "Epoch 4/10\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0037\n",
      "Epoch 5/10\n",
      "800/800 [==============================] - 2s 2ms/step - loss: 0.0037\n",
      "Epoch 6/10\n",
      "800/800 [==============================] - 3s 3ms/step - loss: 0.0045\n",
      "Epoch 7/10\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0042\n",
      "Epoch 8/10\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0038\n",
      "Epoch 9/10\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0037\n",
      "Epoch 10/10\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0051\n",
      "Saving snapshot as altmodel/altmodel-epoch-20.h5\n",
      "Training: epoch 20 -> 30\n",
      "Epoch 1/10\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0041\n",
      "Epoch 2/10\n",
      "800/800 [==============================] - 2s 2ms/step - loss: 0.0037\n",
      "Epoch 3/10\n",
      "800/800 [==============================] - 2s 2ms/step - loss: 0.0037\n",
      "Epoch 4/10\n",
      "800/800 [==============================] - 2s 2ms/step - loss: 0.0037\n",
      "Epoch 5/10\n",
      "800/800 [==============================] - 2s 2ms/step - loss: 0.0037\n",
      "Epoch 6/10\n",
      "800/800 [==============================] - 2s 2ms/step - loss: 0.0044A: 1s \n",
      "Epoch 7/10\n",
      "800/800 [==============================] - 2s 2ms/step - loss: 0.0045\n",
      "Epoch 8/10\n",
      "800/800 [==============================] - 2s 2ms/step - loss: 0.0039\n",
      "Epoch 9/10\n",
      "800/800 [==============================] - 2s 2ms/step - loss: 0.0037\n",
      "Epoch 10/10\n",
      "800/800 [==============================] - 3s 4ms/step - loss: 0.0037\n",
      "Saving snapshot as altmodel/altmodel-epoch-30.h5\n",
      "Training: epoch 30 -> 40\n",
      "Epoch 1/10\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0037\n",
      "Epoch 2/10\n",
      "800/800 [==============================] - 2s 2ms/step - loss: 0.0038\n",
      "Epoch 3/10\n",
      "800/800 [==============================] - 2s 2ms/step - loss: 0.0049\n",
      "Epoch 4/10\n",
      "800/800 [==============================] - 2s 2ms/step - loss: 0.0039\n",
      "Epoch 5/10\n",
      "800/800 [==============================] - 2s 2ms/step - loss: 0.0036\n",
      "Epoch 6/10\n",
      "800/800 [==============================] - 2s 2ms/step - loss: 0.0037\n",
      "Epoch 7/10\n",
      "800/800 [==============================] - 2s 2ms/step - loss: 0.0037\n",
      "Epoch 8/10\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0036\n",
      "Epoch 9/10\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0041\n",
      "Epoch 10/10\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0038\n",
      "Saving snapshot as altmodel/altmodel-epoch-40.h5\n",
      "Training: epoch 40 -> 50\n",
      "Epoch 1/10\n",
      "800/800 [==============================] - 2s 2ms/step - loss: 0.0038\n",
      "Epoch 2/10\n",
      "800/800 [==============================] - 2s 2ms/step - loss: 0.0040\n",
      "Epoch 3/10\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0039\n",
      "Epoch 4/10\n",
      "800/800 [==============================] - 2s 2ms/step - loss: 0.0039\n",
      "Epoch 5/10\n",
      "800/800 [==============================] - 2s 2ms/step - loss: 0.0036\n",
      "Epoch 6/10\n",
      "800/800 [==============================] - 2s 2ms/step - loss: 0.0036\n",
      "Epoch 7/10\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0037\n",
      "Epoch 8/10\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0037\n",
      "Epoch 9/10\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0037\n",
      "Epoch 10/10\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0041\n",
      "Saving snapshot as altmodel/altmodel-epoch-50.h5\n",
      "Training: epoch 50 -> 60\n",
      "Epoch 1/10\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0040\n",
      "Epoch 2/10\n",
      "800/800 [==============================] - 2s 2ms/step - loss: 0.0037\n",
      "Epoch 3/10\n",
      "800/800 [==============================] - 2s 2ms/step - loss: 0.0040\n",
      "Epoch 4/10\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0042\n",
      "Epoch 5/10\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0038\n",
      "Epoch 6/10\n",
      "800/800 [==============================] - 2s 2ms/step - loss: 0.0036\n",
      "Epoch 7/10\n",
      "800/800 [==============================] - 2s 2ms/step - loss: 0.0036\n",
      "Epoch 8/10\n",
      "800/800 [==============================] - 2s 2ms/step - loss: 0.0036\n",
      "Epoch 9/10\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0036\n",
      "Epoch 10/10\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0036\n",
      "Saving snapshot as altmodel/altmodel-epoch-60.h5\n",
      "Training: epoch 60 -> 70\n",
      "Epoch 1/10\n",
      "800/800 [==============================] - 2s 2ms/step - loss: 0.0039\n",
      "Epoch 2/10\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0039\n",
      "Epoch 3/10\n",
      "800/800 [==============================] - 2s 2ms/step - loss: 0.0037\n",
      "Epoch 4/10\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0039\n",
      "Epoch 5/10\n",
      "800/800 [==============================] - 2s 2ms/step - loss: 0.0037\n",
      "Epoch 6/10\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0036\n",
      "Epoch 7/10\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0036\n",
      "Epoch 8/10\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0042\n",
      "Epoch 9/10\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0036\n",
      "Epoch 10/10\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0037\n",
      "Saving snapshot as altmodel/altmodel-epoch-70.h5\n",
      "Training: epoch 70 -> 80\n",
      "Epoch 1/10\n",
      "800/800 [==============================] - 2s 2ms/step - loss: 0.0037\n",
      "Epoch 2/10\n",
      "800/800 [==============================] - 2s 2ms/step - loss: 0.0039\n",
      "Epoch 3/10\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0036\n",
      "Epoch 4/10\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0035\n",
      "Epoch 5/10\n",
      "800/800 [==============================] - 2s 2ms/step - loss: 0.0037\n",
      "Epoch 6/10\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0036\n",
      "Epoch 7/10\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0038\n",
      "Epoch 8/10\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0045\n",
      "Epoch 9/10\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0037\n",
      "Epoch 10/10\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0035\n",
      "Saving snapshot as altmodel/altmodel-epoch-80.h5\n",
      "Training: epoch 80 -> 90\n",
      "Epoch 1/10\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0035\n",
      "Epoch 2/10\n",
      "800/800 [==============================] - 2s 2ms/step - loss: 0.0037\n",
      "Epoch 3/10\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0036\n",
      "Epoch 4/10\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0035\n",
      "Epoch 5/10\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0035\n",
      "Epoch 6/10\n",
      "800/800 [==============================] - 2s 2ms/step - loss: 0.0034\n",
      "Epoch 7/10\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0035\n",
      "Epoch 8/10\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0035\n",
      "Epoch 9/10\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0034\n",
      "Epoch 10/10\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0039\n",
      "Saving snapshot as altmodel/altmodel-epoch-90.h5\n",
      "Training: epoch 90 -> 100\n",
      "Epoch 1/10\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0044\n",
      "Epoch 2/10\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0035\n",
      "Epoch 3/10\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0035\n",
      "Epoch 4/10\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0034\n",
      "Epoch 5/10\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0034\n",
      "Epoch 6/10\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0034\n",
      "Epoch 7/10\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0034\n",
      "Epoch 8/10\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0034\n",
      "Epoch 9/10\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0036\n",
      "Epoch 10/10\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0037\n",
      "Saving snapshot as altmodel/altmodel-epoch-100.h5\n",
      "Training: epoch 100 -> 110\n",
      "Epoch 1/10\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0037\n",
      "Epoch 2/10\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0036\n",
      "Epoch 3/10\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0036\n",
      "Epoch 4/10\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0035\n",
      "Epoch 5/10\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0033\n",
      "Epoch 6/10\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0033\n",
      "Epoch 7/10\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0035\n",
      "Epoch 8/10\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0034\n",
      "Epoch 9/10\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0034\n",
      "Epoch 10/10\n",
      "800/800 [==============================] - 1s 2ms/step - loss: 0.0033\n",
      "Saving snapshot as altmodel/altmodel-epoch-110.h5\n"
     ]
    }
   ],
   "source": [
    "# for i in range(10):\n",
    "#     train(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading altmodel/altmodel-epoch-2.h5\n",
      "Generating prediction\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "((800, 18438), (440, 18438))"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def load_model_from_epoch (epoch):\n",
    "    path = os.path.join('altmodel', 'altmodel-epoch-%s.h5'%(epoch,))\n",
    "    print(\"Loading %s\"%path)\n",
    "    return load_model(path)\n",
    "\n",
    "def predict (epoch):\n",
    "    model = load_model_from_epoch(epoch)\n",
    "    print(\"Generating prediction\")\n",
    "    return map(model.predict, (x_train, x_test))\n",
    "y_train, y_test = predict(2)\n",
    "y_train.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# nobuyuki-data/e333b5c405fbbb98457cfef9186c20c7.json\n",
      "v -0.625 0.095 -1.77223\n",
      "v 0.622197 0.095 -1.775\n",
      "v -0.635 0.472287 -1.795\n",
      "v 0.635 0.472255 -1.795\n",
      "v -0.512952 0.165 1.945\n",
      "v 0.641926 0.225 1.875\n",
      "v -0.58457 0.875 1.825\n",
      "v 0.584568 0.875 1.825\n",
      "v -0.00140141 0.0817718 -1.78684\n",
      "v -0.631677 0.283644 -1.78529\n",
      "v -0.599517 0.0994585 0.0863853\n",
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      "v 0.611846 0.325269 1.90444\n",
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      "v 0.311102 0.484751 1.97571\n",
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      "v 0.168707 0.282697 1.95348\n",
      "v 0.0951965 0.296049 1.9422\n",
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      "v 0.242218 0.273807 1.95563\n",
      "v 0.24403 0.216649 1.94412\n",
      "v 0.313878 0.366375 1.95081\n",
      "v 0.238593 0.368609 1.95661\n",
      "v 0.240405 0.324554 1.95595\n",
      "v 0.314803 0.318992 1.95026\n",
      "v 0.0880227 0.373077 1.95783\n",
      "v 0.0916096 0.335678 1.96252\n",
      "v 0.166007 0.330116 1.95937\n",
      "v 0.163308 0.370843 1.95596\n",
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      "v 0.557995 0.81704 1.84529\n",
      "v 0.332341 0.879109 1.85975\n",
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      "v 0.405324 0.874305 1.85531\n",
      "v 0.353476 0.7408 1.89102\n",
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      "v 0.419058 0.782905 1.87718\n",
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      "v 0.571583 0.733954 1.86059\n",
      "v 0.565261 0.775331 1.85486\n",
      "v 0.491781 0.779118 1.86861\n",
      "v 0.498407 0.736236 1.8752\n",
      "v 0.0387435 0.889765 1.86956\n",
      "v 0.043162 0.842304 1.87514\n",
      "v 0.117397 0.84026 1.87441\n",
      "v 0.112203 0.887485 1.86865\n",
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      "v 0.0696724 0.559442 1.97241\n",
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      "v 0.065254 0.606451 1.905\n",
      "v 0.306341 0.56379 1.95346\n",
      "v 0.299595 0.608246 1.89346\n",
      "v 0.221482 0.607647 1.89959\n",
      "v 0.227452 0.562341 1.961\n",
      "v 0.286104 0.697157 1.89707\n",
      "v 0.209542 0.698261 1.90048\n",
      "v 0.215512 0.652954 1.90141\n",
      "v 0.29285 0.652701 1.89732\n",
      "v 0.0564172 0.700469 1.90405\n",
      "v 0.0608356 0.65346 1.90515\n",
      "v 0.138174 0.653207 1.90392\n",
      "v 0.132979 0.699365 1.90275\n",
      "v 0.609852 0.569588 1.89593\n",
      "v 0.587722 0.610638 1.8543\n",
      "v 0.52589 0.61004 1.87045\n",
      "v 0.540328 0.568138 1.91717\n",
      "v 0.57628 0.69274 1.86216\n",
      "v 0.505958 0.693844 1.87659\n",
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      "v 0.580521 0.651689 1.86277\n",
      "v 0.360998 0.696052 1.89192\n",
      "v 0.368996 0.652448 1.89167\n",
      "v 0.443951 0.652195 1.88568\n",
      "v 0.434223 0.694948 1.88554\n",
      "v 0.384993 0.56524 1.94355\n",
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      "v -0.322889 0.549419 1.96903\n",
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      "v -0.399204 0.592684 1.89128\n",
      "v -0.400861 0.547353 1.96366\n",
      "v -0.323467 0.68656 1.89477\n",
      "v -0.39589 0.683347 1.88914\n",
      "v -0.397547 0.638015 1.88993\n",
      "v -0.323275 0.640847 1.89516\n",
      "v -0.535107 0.676919 1.87215\n",
      "v -0.528102 0.632353 1.87475\n",
      "v -0.465638 0.635184 1.88379\n",
      "v -0.466252 0.680133 1.88239\n",
      "v -0.00903875 0.557684 1.97504\n",
      "v -0.0127052 0.604928 1.90689\n",
      "v -0.0905097 0.602479 1.90633\n",
      "v -0.0875715 0.555618 1.97714\n",
      "v -0.020038 0.699415 1.90449\n",
      "v -0.0963861 0.696201 1.90357\n",
      "v -0.0934479 0.64934 1.90477\n",
      "v -0.0163716 0.652171 1.90566\n",
      "v -0.249082 0.689774 1.89927\n",
      "v -0.247601 0.643678 1.90034\n",
      "v -0.170524 0.646509 1.90323\n",
      "v -0.172734 0.692987 1.90207\n",
      "v -0.244637 0.551486 1.9739\n",
      "v -0.166104 0.553552 1.97651\n",
      "v -0.168314 0.60003 1.90426\n",
      "v -0.246119 0.597582 1.90124\n",
      "v -0.0347036 0.889657 1.86963\n",
      "v -0.108139 0.887161 1.8688\n",
      "v -0.105201 0.839222 1.87497\n",
      "v -0.0310372 0.841957 1.87537\n",
      "v -0.255009 0.881976 1.86364\n",
      "v -0.253527 0.833803 1.8715\n",
      "v -0.179364 0.836504 1.87362\n",
      "v -0.181574 0.884366 1.86701\n",
      "v -0.250564 0.73702 1.89737\n",
      "v -0.174944 0.740287 1.90004\n",
      "v -0.177154 0.788409 1.88971\n",
      "v -0.252046 0.785416 1.88786\n",
      "v -0.0237044 0.746822 1.90247\n",
      "v -0.0273708 0.794394 1.89057\n",
      "v -0.102262 0.791401 1.8906\n",
      "v -0.0993242 0.743555 1.90173\n",
      "f 1 1541 4611 1540\n",
      "f 389 3079 4611 1541\n",
      "f 1155 3077 4611 3079\n",
      "f 388 1540 4611 3077\n",
      "f 101 1933 4612 1931\n",
      "f 775 3851 4612 1933\n",
      "f 1155 3079 4612 3851\n",
      "f 389 1931 4612 3079\n",
      "f 291 2691 4613 2692\n",
      "f 773 3847 4613 2691\n",
      "f 1155 3851 4613 3847\n",
      "f 775 2692 4613 3851\n",
      "f 100 1927 4614 1929\n",
      "f 388 3077 4614 1927\n",
      "f 1155 3847 4614 3077\n",
      "f 773 1929 4614 3847\n",
      "f 29 1645 4615 1643\n",
      "f 493 3287 4615 1645\n",
      "f 1156 3283 4615 3287\n",
      "f 491 1643 4615 3283\n",
      "f 199 2325 4616 2323\n",
      "f 971 4243 4616 2325\n",
      "f 1156 3287 4616 4243\n",
      "f 493 2323 4616 3287\n",
      "f 291 2692 4617 2694\n",
      "f 775 3852 4617 2692\n",
      "f 1156 4243 4617 3852\n",
      "f 971 2694 4617 4243\n",
      "f 101 1932 4618 1933\n",
      "f 491 3283 4618 1932\n",
      "f 1156 3852 4618 3283\n",
      "f 775 1933 4618 3852\n",
      "f 75 1827 4619 1828\n",
      "f 675 3651 4619 1827\n",
      "f 1157 3653 4619 3651\n",
      "f 676 1828 4619 3653\n",
      "f 197 2317 4620 2316\n",
      "f 967 4235 4620 2317\n",
      "f 1157 3651 4620 4235\n",
      "f 675 2316 4620 3651\n",
      "f 291 2694 4621 2693\n",
      "f 971 4244 4621 2694\n",
      "f 1157 4235 4621 4244\n",
      "f 967 2693 4621 4235\n",
      "f 199 2324 4622 2325\n",
      "f 676 3653 4622 2324\n",
      "f 1157 4244 4622 3653\n",
      "f 971 2325 4622 4244\n",
      "f 28 1639 4623 1641\n",
      "f 487 3275 4623 1639\n",
      "f 1158 3279 4623 3275\n",
      "f 489 1641 4623 3279\n",
      "f 100 1929 4624 1928\n",
      "f 773 3848 4624 1929\n",
      "f 1158 3275 4624 3848\n",
      "f 487 1928 4624 3275\n",
      "f 291 2693 4625 2691\n",
      "f 967 4236 4625 2693\n",
      "f 1158 3848 4625 4236\n",
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      "f 110 1969 4932 1967\n",
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      "f 121 2013 4996 2011\n",
      "f 815 3931 4996 2013\n",
      "f 1251 3119 4996 3931\n",
      "f 409 2011 4996 3119\n",
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      "f 813 3927 4997 2787\n",
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      "f 815 2788 4997 3931\n",
      "f 120 2007 4998 2009\n",
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      "f 813 2009 4998 3927\n",
      "f 49 1725 4999 1723\n",
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      "f 239 2485 5000 2483\n",
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      "f 1051 2790 5001 4403\n",
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      "f 120 2009 5008 2008\n",
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      "f 315 2789 5009 2787\n",
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      "f 19 1605 5011 1604\n",
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      "f 81 1852 5019 1854\n",
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      "f 1257 3705 5019 3702\n",
      "f 702 1854 5019 3705\n",
      "f 239 2486 5020 2484\n",
      "f 1052 4405 5020 2486\n",
      "f 1257 3702 5020 4405\n",
      "f 700 2484 5020 3702\n",
      "f 316 2794 5021 2793\n",
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      "f 1257 4405 5021 4580\n",
      "f 1052 2793 5021 4405\n",
      "f 283 2660 5022 2661\n",
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      "f 1139 2661 5022 4580\n",
      "f 49 1724 5023 1725\n",
      "f 572 3445 5023 1724\n",
      "f 1258 3448 5023 3445\n",
      "f 573 1725 5023 3448\n",
      "f 164 2185 5024 2184\n",
      "f 901 4104 5024 2185\n",
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      "f 572 2184 5024 3445\n",
      "f 316 2793 5025 2791\n",
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      "f 901 2791 5025 4104\n",
      "f 239 2483 5026 2486\n",
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      "f 1258 4406 5026 3448\n",
      "f 1052 2486 5026 4406\n",
      "f 22 1617 5027 1618\n",
      "f 465 3232 5027 1617\n",
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      "f 177 2238 5028 2235\n",
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      "f 465 2235 5028 3232\n",
      "f 317 2796 5029 2795\n",
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      "f 178 2239 5030 2241\n",
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      "f 65 1790 5031 1788\n",
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      "f 272 2617 5032 2615\n",
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      "f 81 1854 5035 1853\n",
      "f 702 3706 5035 1854\n",
      "f 1261 3703 5035 3706\n",
      "f 701 1853 5035 3703\n",
      "f 283 2662 5036 2660\n",
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      "f 702 2660 5036 3706\n",
      "f 317 2797 5037 2798\n",
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      "f 272 2616 5038 2617\n",
      "f 701 3703 5038 2616\n",
      "f 1261 4536 5038 3703\n",
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      "f 71 1812 5039 1813\n",
      "f 660 3621 5039 1812\n",
      "f 1262 3624 5039 3621\n",
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      "f 178 2241 5040 2240\n",
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      "f 660 2240 5040 3621\n",
      "f 317 2798 5041 2796\n",
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      "f 929 2796 5041 4160\n",
      "f 283 2659 5042 2662\n",
      "f 661 3624 5042 2659\n",
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      "f 1140 2662 5042 4582\n",
      "f 16 1592 5043 1593\n",
      "f 440 3181 5043 1592\n",
      "f 1263 3184 5043 3181\n",
      "f 441 1593 5043 3184\n",
      "f 152 2137 5044 2135\n",
      "f 877 4055 5044 2137\n",
      "f 1263 3181 5044 4055\n",
      "f 440 2135 5044 3181\n",
      "f 318 2800 5045 2799\n",
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      "f 153 2139 5046 2142\n",
      "f 441 3184 5046 2139\n",
      "f 1263 4061 5046 3184\n",
      "f 880 2142 5046 4061\n",
      "f 48 1721 5047 1720\n",
      "f 569 3440 5047 1721\n",
      "f 1264 3437 5047 3440\n",
      "f 568 1720 5047 3437\n",
      "f 237 2478 5048 2475\n",
      "f 1048 4397 5048 2478\n",
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      "f 569 2475 5048 3440\n",
      "f 318 2799 5049 2801\n",
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      "f 1048 2801 5049 4397\n",
      "f 152 2136 5050 2137\n",
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      "f 1264 4056 5050 3437\n",
      "f 877 2137 5050 4056\n",
      "f 81 1853 5051 1851\n",
      "f 701 3704 5051 1853\n",
      "f 1265 3700 5051 3704\n",
      "f 699 1851 5051 3700\n",
      "f 272 2618 5052 2616\n",
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      "f 1265 3704 5052 4537\n",
      "f 701 2616 5052 3704\n",
      "f 318 2801 5053 2802\n",
      "f 1048 4398 5053 2801\n",
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      "f 65 1787 5055 1790\n",
      "f 635 3572 5055 1787\n",
      "f 1266 3578 5055 3572\n",
      "f 638 1790 5055 3578\n",
      "f 153 2142 5056 2140\n",
      "f 880 4062 5056 2142\n",
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      "f 635 2140 5056 3572\n",
      "f 318 2802 5057 2800\n",
      "f 1118 4538 5057 2802\n",
      "f 1266 4062 5057 4538\n",
      "f 880 2800 5057 4062\n",
      "f 272 2615 5058 2618\n",
      "f 638 3578 5058 2615\n",
      "f 1266 4538 5058 3578\n",
      "f 1118 2618 5058 4538\n",
      "f 6 1554 5059 1556\n",
      "f 402 3105 5059 1554\n",
      "f 1267 3109 5059 3105\n",
      "f 404 1556 5059 3109\n",
      "f 114 1985 5060 1983\n",
      "f 801 3903 5060 1985\n",
      "f 1267 3105 5060 3903\n",
      "f 402 1983 5060 3105\n",
      "f 319 2804 5061 2803\n",
      "f 805 3911 5061 2804\n",
      "f 1267 3903 5061 3911\n",
      "f 801 2803 5061 3903\n",
      "f 116 1991 5062 1993\n",
      "f 404 3109 5062 1991\n",
      "f 1267 3911 5062 3109\n",
      "f 805 1993 5062 3911\n",
      "f 42 1697 5063 1695\n",
      "f 545 3391 5063 1697\n",
      "f 1268 3387 5063 3391\n",
      "f 543 1695 5063 3387\n",
      "f 225 2429 5064 2427\n",
      "f 1023 4347 5064 2429\n",
      "f 1268 3391 5064 4347\n",
      "f 545 2427 5064 3391\n",
      "f 319 2803 5065 2805\n",
      "f 801 3904 5065 2803\n",
      "f 1268 4347 5065 3904\n",
      "f 1023 2805 5065 4347\n",
      "f 114 1984 5066 1985\n",
      "f 543 3387 5066 1984\n",
      "f 1268 3904 5066 3387\n",
      "f 801 1985 5066 3904\n",
      "f 82 1856 5067 1855\n",
      "f 704 3709 5067 1856\n",
      "f 1269 3707 5067 3709\n",
      "f 703 1855 5067 3707\n",
      "f 229 2445 5068 2444\n",
      "f 1031 4363 5068 2445\n",
      "f 1269 3709 5068 4363\n",
      "f 704 2444 5068 3709\n",
      "f 319 2805 5069 2806\n",
      "f 1023 4348 5069 2805\n",
      "f 1269 4363 5069 4348\n",
      "f 1031 2806 5069 4363\n",
      "f 225 2428 5070 2429\n",
      "f 703 3707 5070 2428\n",
      "f 1269 4348 5070 3707\n",
      "f 1023 2429 5070 4348\n",
      "f 44 1703 5071 1705\n",
      "f 551 3403 5071 1703\n",
      "f 1270 3407 5071 3403\n",
      "f 553 1705 5071 3407\n",
      "f 116 1993 5072 1992\n",
      "f 805 3912 5072 1993\n",
      "f 1270 3403 5072 3912\n",
      "f 551 1992 5072 3403\n",
      "f 319 2806 5073 2804\n",
      "f 1031 4364 5073 2806\n",
      "f 1270 3912 5073 4364\n",
      "f 805 2804 5073 3912\n",
      "f 229 2443 5074 2445\n",
      "f 553 3407 5074 2443\n",
      "f 1270 4364 5074 3407\n",
      "f 1031 2445 5074 4364\n",
      "f 13 1581 5075 1580\n",
      "f 429 3160 5075 1581\n",
      "f 1271 3157 5075 3160\n",
      "f 428 1580 5075 3157\n",
      "f 141 2094 5076 2091\n",
      "f 856 4013 5076 2094\n",
      "f 1271 3160 5076 4013\n",
      "f 429 2091 5076 3160\n",
      "f 320 2807 5077 2808\n",
      "f 853 4007 5077 2807\n",
      "f 1271 4013 5077 4007\n",
      "f 856 2808 5077 4013\n",
      "f 140 2087 5078 2089\n",
      "f 428 3157 5078 2087\n",
      "f 1271 4007 5078 3157\n",
      "f 853 2089 5078 4007\n",
      "f 59 1766 5079 1763\n",
      "f 614 3529 5079 1766\n",
      "f 1272 3524 5079 3529\n",
      "f 611 1763 5079 3524\n",
      "f 260 2569 5080 2567\n",
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      "f 1272 3529 5080 4487\n",
      "f 614 2567 5080 3529\n",
      "f 320 2808 5081 2810\n",
      "f 856 4014 5081 2808\n",
      "f 1272 4487 5081 4014\n",
      "f 1093 2810 5081 4487\n",
      "f 141 2092 5082 2094\n",
      "f 611 3524 5082 2092\n",
      "f 1272 4014 5082 3524\n",
      "f 856 2094 5082 4014\n",
      "f 82 1855 5083 1857\n",
      "f 703 3708 5083 1855\n",
      "f 1273 3711 5083 3708\n",
      "f 705 1857 5083 3711\n",
      "f 225 2430 5084 2428\n",
      "f 1024 4349 5084 2430\n",
      "f 1273 3708 5084 4349\n",
      "f 703 2428 5084 3708\n",
      "f 320 2810 5085 2809\n",
      "f 1093 4488 5085 2810\n",
      "f 1273 4349 5085 4488\n",
      "f 1024 2809 5085 4349\n",
      "f 260 2568 5086 2569\n",
      "f 705 3711 5086 2568\n",
      "f 1273 4488 5086 3711\n",
      "f 1093 2569 5086 4488\n",
      "f 42 1696 5087 1697\n",
      "f 544 3389 5087 1696\n",
      "f 1274 3392 5087 3389\n",
      "f 545 1697 5087 3392\n",
      "f 140 2089 5088 2088\n",
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      "f 1274 3389 5088 4008\n",
      "f 544 2088 5088 3389\n",
      "f 320 2809 5089 2807\n",
      "f 1024 4350 5089 2809\n",
      "f 1274 4008 5089 4350\n",
      "f 853 2807 5089 4008\n",
      "f 225 2427 5090 2430\n",
      "f 545 3392 5090 2427\n",
      "f 1274 4350 5090 3392\n",
      "f 1024 2430 5090 4350\n",
      "f 22 1618 5091 1616\n",
      "f 466 3234 5091 1618\n",
      "f 1275 3230 5091 3234\n",
      "f 464 1616 5091 3230\n",
      "f 178 2242 5092 2239\n",
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      "f 321 2811 5093 2812\n",
      "f 926 4153 5093 2811\n",
      "f 1275 4161 5093 4153\n",
      "f 930 2812 5093 4161\n",
      "f 176 2231 5094 2234\n",
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      "f 1275 4153 5094 3230\n",
      "f 926 2234 5094 4153\n",
      "f 71 1814 5095 1812\n",
      "f 662 3625 5095 1814\n",
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      "f 284 2665 5096 2663\n",
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      "f 321 2812 5097 2814\n",
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      "f 178 2240 5098 2242\n",
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      "f 82 1857 5099 1858\n",
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      "f 706 1858 5099 3713\n",
      "f 260 2570 5100 2568\n",
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      "f 705 2568 5100 3712\n",
      "f 321 2814 5101 2813\n",
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      "f 1094 2813 5101 4489\n",
      "f 284 2664 5102 2665\n",
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      "f 1277 4584 5102 3713\n",
      "f 1141 2665 5102 4584\n",
      "f 59 1764 5103 1766\n",
      "f 612 3526 5103 1764\n",
      "f 1278 3530 5103 3526\n",
      "f 614 1766 5103 3530\n",
      "f 176 2234 5104 2232\n",
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      "f 612 2232 5104 3526\n",
      "f 321 2813 5105 2811\n",
      "f 1094 4490 5105 2813\n",
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      "f 926 2811 5105 4154\n",
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      "f 1094 2570 5106 4490\n",
      "f 19 1603 5107 1605\n",
      "f 451 3203 5107 1603\n",
      "f 1279 3208 5107 3203\n",
      "f 453 1605 5107 3208\n",
      "f 163 2181 5108 2179\n",
      "f 899 4099 5108 2181\n",
      "f 1279 3203 5108 4099\n",
      "f 451 2179 5108 3203\n",
      "f 322 2816 5109 2815\n",
      "f 904 4109 5109 2816\n",
      "f 1279 4099 5109 4109\n",
      "f 899 2815 5109 4099\n",
      "f 165 2187 5110 2190\n",
      "f 453 3208 5110 2187\n",
      "f 1279 4109 5110 3208\n",
      "f 904 2190 5110 4109\n",
      "f 44 1705 5111 1704\n",
      "f 553 3408 5111 1705\n",
      "f 1280 3405 5111 3408\n",
      "f 552 1704 5111 3405\n",
      "f 229 2446 5112 2443\n",
      "f 1032 4365 5112 2446\n",
      "f 1280 3408 5112 4365\n",
      "f 553 2443 5112 3408\n",
      "f 322 2815 5113 2817\n",
      "f 899 4100 5113 2815\n",
      "f 1280 4365 5113 4100\n",
      "f 1032 2817 5113 4365\n",
      "f 163 2180 5114 2181\n",
      "f 552 3405 5114 2180\n",
      "f 1280 4100 5114 3405\n",
      "f 899 2181 5114 4100\n",
      "f 82 1858 5115 1856\n",
      "f 706 3714 5115 1858\n",
      "f 1281 3710 5115 3714\n",
      "f 704 1856 5115 3710\n",
      "f 284 2666 5116 2664\n",
      "f 1142 4585 5116 2666\n",
      "f 1281 3714 5116 4585\n",
      "f 706 2664 5116 3714\n",
      "f 322 2817 5117 2818\n",
      "f 1032 4366 5117 2817\n",
      "f 1281 4585 5117 4366\n",
      "f 1142 2818 5117 4585\n",
      "f 229 2444 5118 2446\n",
      "f 704 3710 5118 2444\n",
      "f 1281 4366 5118 3710\n",
      "f 1032 2446 5118 4366\n",
      "f 71 1811 5119 1814\n",
      "f 659 3620 5119 1811\n",
      "f 1282 3626 5119 3620\n",
      "f 662 1814 5119 3626\n",
      "f 165 2190 5120 2188\n",
      "f 904 4110 5120 2190\n",
      "f 1282 3620 5120 4110\n",
      "f 659 2188 5120 3620\n",
      "f 322 2818 5121 2816\n",
      "f 1142 4586 5121 2818\n",
      "f 1282 4110 5121 4586\n",
      "f 904 2816 5121 4110\n",
      "f 284 2663 5122 2666\n",
      "f 662 3626 5122 2663\n",
      "f 1282 4586 5122 3626\n",
      "f 1142 2666 5122 4586\n",
      "f 1 1539 5123 1541\n",
      "f 387 3075 5123 1539\n",
      "f 1283 3080 5123 3075\n",
      "f 389 1541 5123 3080\n",
      "f 99 1925 5124 1923\n",
      "f 771 3843 5124 1925\n",
      "f 1283 3075 5124 3843\n",
      "f 387 1923 5124 3075\n",
      "f 323 2820 5125 2819\n",
      "f 776 3853 5125 2820\n",
      "f 1283 3843 5125 3853\n",
      "f 771 2819 5125 3843\n",
      "f 101 1931 5126 1934\n",
      "f 389 3080 5126 1931\n",
      "f 1283 3853 5126 3080\n",
      "f 776 1934 5126 3853\n",
      "f 27 1637 5127 1635\n",
      "f 485 3271 5127 1637\n",
      "f 1284 3267 5127 3271\n",
      "f 483 1635 5127 3267\n",
      "f 195 2309 5128 2307\n",
      "f 963 4227 5128 2309\n",
      "f 1284 3271 5128 4227\n",
      "f 485 2307 5128 3271\n",
      "f 323 2819 5129 2821\n",
      "f 771 3844 5129 2819\n",
      "f 1284 4227 5129 3844\n",
      "f 963 2821 5129 4227\n",
      "f 99 1924 5130 1925\n",
      "f 483 3267 5130 1924\n",
      "f 1284 3844 5130 3267\n",
      "f 771 1925 5130 3844\n",
      "f 83 1860 5131 1859\n",
      "f 708 3717 5131 1860\n",
      "f 1285 3715 5131 3717\n",
      "f 707 1859 5131 3715\n",
      "f 200 2329 5132 2328\n",
      "f 973 4247 5132 2329\n",
      "f 1285 3717 5132 4247\n",
      "f 708 2328 5132 3717\n",
      "f 323 2821 5133 2822\n",
      "f 963 4228 5133 2821\n",
      "f 1285 4247 5133 4228\n",
      "f 973 2822 5133 4247\n",
      "f 195 2308 5134 2309\n",
      "f 707 3715 5134 2308\n",
      "f 1285 4228 5134 3715\n",
      "f 963 2309 5134 4228\n",
      "f 29 1643 5135 1646\n",
      "f 491 3284 5135 1643\n",
      "f 1286 3289 5135 3284\n",
      "f 494 1646 5135 3289\n",
      "f 101 1934 5136 1932\n",
      "f 776 3854 5136 1934\n",
      "f 1286 3284 5136 3854\n",
      "f 491 1932 5136 3284\n",
      "f 323 2822 5137 2820\n",
      "f 973 4248 5137 2822\n",
      "f 1286 3854 5137 4248\n",
      "f 776 2820 5137 3854\n",
      "f 200 2327 5138 2329\n",
      "f 494 3289 5138 2327\n",
      "f 1286 4248 5138 3289\n",
      "f 973 2329 5138 4248\n",
      "f 9 1565 5139 1563\n",
      "f 413 3127 5139 1565\n",
      "f 1287 3123 5139 3127\n",
      "f 411 1563 5139 3123\n",
      "f 125 2029 5140 2027\n",
      "f 823 3947 5140 2029\n",
      "f 1287 3127 5140 3947\n",
      "f 413 2027 5140 3127\n",
      "f 324 2823 5141 2824\n",
      "f 819 3939 5141 2823\n",
      "f 1287 3947 5141 3939\n",
      "f 823 2824 5141 3947\n",
      "f 123 2019 5142 2021\n",
      "f 411 3123 5142 2019\n",
      "f 1287 3939 5142 3123\n",
      "f 819 2021 5142 3939\n",
      "f 51 1733 5143 1731\n",
      "f 581 3463 5143 1733\n",
      "f 1288 3459 5143 3463\n",
      "f 579 1731 5143 3459\n",
      "f 243 2501 5144 2499\n",
      "f 1059 4419 5144 2501\n",
      "f 1288 3463 5144 4419\n",
      "f 581 2499 5144 3463\n",
      "f 324 2824 5145 2826\n",
      "f 823 3948 5145 2824\n",
      "f 1288 4419 5145 3948\n",
      "f 1059 2826 5145 4419\n",
      "f 125 2028 5146 2029\n",
      "f 579 3459 5146 2028\n",
      "f 1288 3948 5146 3459\n",
      "f 823 2029 5146 3948\n",
      "f 83 1859 5147 1861\n",
      "f 707 3716 5147 1859\n",
      "f 1289 3719 5147 3716\n",
      "f 709 1861 5147 3719\n",
      "f 195 2310 5148 2308\n",
      "f 964 4229 5148 2310\n",
      "f 1289 3716 5148 4229\n",
      "f 707 2308 5148 3716\n",
      "f 324 2826 5149 2825\n",
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      "f 964 2825 5149 4229\n",
      "f 243 2500 5150 2501\n",
      "f 709 3719 5150 2500\n",
      "f 1289 4420 5150 3719\n",
      "f 1059 2501 5150 4420\n",
      "f 27 1636 5151 1637\n",
      "f 484 3269 5151 1636\n",
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      "f 403 1555 5955 3108\n",
      "f 116 1994 5956 1991\n",
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      "f 404 1991 5956 3110\n",
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      "f 806 3028 5957 3913\n",
      "f 115 1987 5958 1990\n",
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      "f 116 1992 5962 1994\n",
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      "f 115 1990 5968 1988\n",
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      "f 122 2018 6020 2015\n",
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      "f 121 2011 6022 2014\n",
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      "f 122 2016 6026 2018\n",
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      "f 121 2014 6032 2012\n",
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      "f 572 3446 6074 2184\n",
      "f 1520 4106 6074 3446\n",
      "f 902 2186 6074 4106\n",
      "f 97 1917 6075 1915\n",
      "f 765 3832 6075 1917\n",
      "f 1521 3828 6075 3832\n",
      "f 763 1915 6075 3828\n",
      "f 286 2674 6076 2672\n",
      "f 1146 4593 6076 2674\n",
      "f 1521 3832 6076 4593\n",
      "f 765 2672 6076 3832\n",
      "f 382 3057 6077 3058\n",
      "f 1054 4410 6077 3057\n",
      "f 1521 4593 6077 4410\n",
      "f 1146 3058 6077 4593\n",
      "f 240 2488 6078 2490\n",
      "f 763 3828 6078 2488\n",
      "f 1521 4410 6078 3828\n",
      "f 1054 2490 6078 4410\n",
      "f 72 1815 6079 1818\n",
      "f 663 3628 6079 1815\n",
      "f 1522 3634 6079 3628\n",
      "f 666 1818 6079 3634\n",
      "f 166 2194 6080 2192\n",
      "f 906 4114 6080 2194\n",
      "f 1522 3628 6080 4114\n",
      "f 663 2192 6080 3628\n",
      "f 382 3058 6081 3056\n",
      "f 1146 4594 6081 3058\n",
      "f 1522 4114 6081 4594\n",
      "f 906 3056 6081 4114\n",
      "f 286 2671 6082 2674\n",
      "f 666 3634 6082 2671\n",
      "f 1522 4594 6082 3634\n",
      "f 1146 2674 6082 4594\n",
      "f 7 1558 6083 1559\n",
      "f 406 3114 6083 1558\n",
      "f 1523 3116 6083 3114\n",
      "f 407 1559 6083 3116\n",
      "f 118 2002 6084 1999\n",
      "f 810 3921 6084 2002\n",
      "f 1523 3114 6084 3921\n",
      "f 406 1999 6084 3114\n",
      "f 383 3060 6085 3059\n",
      "f 812 3925 6085 3060\n",
      "f 1523 3921 6085 3925\n",
      "f 810 3059 6085 3921\n",
      "f 119 2003 6086 2006\n",
      "f 407 3116 6086 2003\n",
      "f 1523 3925 6086 3116\n",
      "f 812 2006 6086 3925\n",
      "f 46 1714 6087 1711\n",
      "f 562 3425 6087 1714\n",
      "f 1524 3420 6087 3425\n",
      "f 559 1711 6087 3420\n",
      "f 234 2465 6088 2463\n",
      "f 1041 4383 6088 2465\n",
      "f 1524 3425 6088 4383\n",
      "f 562 2463 6088 3425\n",
      "f 383 3059 6089 3061\n",
      "f 810 3922 6089 3059\n",
      "f 1524 4383 6089 3922\n",
      "f 1041 3061 6089 4383\n",
      "f 118 2000 6090 2002\n",
      "f 559 3420 6090 2000\n",
      "f 1524 3922 6090 3420\n",
      "f 810 2002 6090 3922\n",
      "f 98 1920 6091 1919\n",
      "f 768 3837 6091 1920\n",
      "f 1525 3835 6091 3837\n",
      "f 767 1919 6091 3835\n",
      "f 236 2473 6092 2472\n",
      "f 1045 4391 6092 2473\n",
      "f 1525 3837 6092 4391\n",
      "f 768 2472 6092 3837\n",
      "f 383 3061 6093 3062\n",
      "f 1041 4384 6093 3061\n",
      "f 1525 4391 6093 4384\n",
      "f 1045 3062 6093 4391\n",
      "f 234 2464 6094 2465\n",
      "f 767 3835 6094 2464\n",
      "f 1525 4384 6094 3835\n",
      "f 1041 2465 6094 4384\n",
      "f 47 1715 6095 1718\n",
      "f 563 3428 6095 1715\n",
      "f 1526 3433 6095 3428\n",
      "f 566 1718 6095 3433\n",
      "f 119 2006 6096 2004\n",
      "f 812 3926 6096 2006\n",
      "f 1526 3428 6096 3926\n",
      "f 563 2004 6096 3428\n",
      "f 383 3062 6097 3060\n",
      "f 1045 4392 6097 3062\n",
      "f 1526 3926 6097 4392\n",
      "f 812 3060 6097 3926\n",
      "f 236 2471 6098 2473\n",
      "f 566 3433 6098 2471\n",
      "f 1526 4392 6098 3433\n",
      "f 1045 2473 6098 4392\n",
      "f 18 1602 6099 1600\n",
      "f 450 3202 6099 1602\n",
      "f 1527 3198 6099 3202\n",
      "f 448 1600 6099 3198\n",
      "f 162 2178 6100 2175\n",
      "f 898 4097 6100 2178\n",
      "f 1527 3202 6100 4097\n",
      "f 450 2175 6100 3202\n",
      "f 384 3063 6101 3064\n",
      "f 894 4089 6101 3063\n",
      "f 1527 4097 6101 4089\n",
      "f 898 3064 6101 4097\n",
      "f 160 2167 6102 2170\n",
      "f 448 3198 6102 2167\n",
      "f 1527 4089 6102 3198\n",
      "f 894 2170 6102 4089\n",
      "f 70 1810 6103 1807\n",
      "f 658 3617 6103 1810\n",
      "f 1528 3612 6103 3617\n",
      "f 655 1807 6103 3612\n",
      "f 282 2657 6104 2655\n",
      "f 1137 4575 6104 2657\n",
      "f 1528 3617 6104 4575\n",
      "f 658 2655 6104 3617\n",
      "f 384 3064 6105 3066\n",
      "f 898 4098 6105 3064\n",
      "f 1528 4575 6105 4098\n",
      "f 1137 3066 6105 4575\n",
      "f 162 2176 6106 2178\n",
      "f 655 3612 6106 2176\n",
      "f 1528 4098 6106 3612\n",
      "f 898 2178 6106 4098\n",
      "f 98 1919 6107 1921\n",
      "f 767 3836 6107 1919\n",
      "f 1529 3839 6107 3836\n",
      "f 769 1921 6107 3839\n",
      "f 234 2466 6108 2464\n",
      "f 1042 4385 6108 2466\n",
      "f 1529 3836 6108 4385\n",
      "f 767 2464 6108 3836\n",
      "f 384 3066 6109 3065\n",
      "f 1137 4576 6109 3066\n",
      "f 1529 4385 6109 4576\n",
      "f 1042 3065 6109 4385\n",
      "f 282 2656 6110 2657\n",
      "f 769 3839 6110 2656\n",
      "f 1529 4576 6110 3839\n",
      "f 1137 2657 6110 4576\n",
      "f 46 1712 6111 1714\n",
      "f 560 3422 6111 1712\n",
      "f 1530 3426 6111 3422\n",
      "f 562 1714 6111 3426\n",
      "f 160 2170 6112 2168\n",
      "f 894 4090 6112 2170\n",
      "f 1530 3422 6112 4090\n",
      "f 560 2168 6112 3422\n",
      "f 384 3065 6113 3063\n",
      "f 1042 4386 6113 3065\n",
      "f 1530 4090 6113 4386\n",
      "f 894 3063 6113 4090\n",
      "f 234 2463 6114 2466\n",
      "f 562 3426 6114 2463\n",
      "f 1530 4386 6114 3426\n",
      "f 1042 2466 6114 4386\n",
      "f 26 1634 6115 1632\n",
      "f 482 3266 6115 1634\n",
      "f 1531 3262 6115 3266\n",
      "f 480 1632 6115 3262\n",
      "f 194 2306 6116 2303\n",
      "f 962 4225 6116 2306\n",
      "f 1531 3266 6116 4225\n",
      "f 482 2303 6116 3266\n",
      "f 385 3067 6117 3068\n",
      "f 958 4217 6117 3067\n",
      "f 1531 4225 6117 4217\n",
      "f 962 3068 6117 4225\n",
      "f 192 2295 6118 2298\n",
      "f 480 3262 6118 2295\n",
      "f 1531 4217 6118 3262\n",
      "f 958 2298 6118 4217\n",
      "f 74 1826 6119 1824\n",
      "f 674 3649 6119 1826\n",
      "f 1532 3646 6119 3649\n",
      "f 672 1824 6119 3646\n",
      "f 290 2689 6120 2687\n",
      "f 1153 4607 6120 2689\n",
      "f 1532 3649 6120 4607\n",
      "f 674 2687 6120 3649\n",
      "f 385 3068 6121 3070\n",
      "f 962 4226 6121 3068\n",
      "f 1532 4607 6121 4226\n",
      "f 1153 3070 6121 4607\n",
      "f 194 2304 6122 2306\n",
      "f 672 3646 6122 2304\n",
      "f 1532 4226 6122 3646\n",
      "f 962 2306 6122 4226\n",
      "f 98 1921 6123 1922\n",
      "f 769 3840 6123 1921\n",
      "f 1533 3841 6123 3840\n",
      "f 770 1922 6123 3841\n",
      "f 282 2658 6124 2656\n",
      "f 1138 4577 6124 2658\n",
      "f 1533 3840 6124 4577\n",
      "f 769 2656 6124 3840\n",
      "f 385 3070 6125 3069\n",
      "f 1153 4608 6125 3070\n",
      "f 1533 4577 6125 4608\n",
      "f 1138 3069 6125 4577\n",
      "f 290 2688 6126 2689\n",
      "f 770 3841 6126 2688\n",
      "f 1533 4608 6126 3841\n",
      "f 1153 2689 6126 4608\n",
      "f 70 1808 6127 1810\n",
      "f 656 3614 6127 1808\n",
      "f 1534 3618 6127 3614\n",
      "f 658 1810 6127 3618\n",
      "f 192 2298 6128 2296\n",
      "f 958 4218 6128 2298\n",
      "f 1534 3614 6128 4218\n",
      "f 656 2296 6128 3614\n",
      "f 385 3069 6129 3067\n",
      "f 1138 4578 6129 3069\n",
      "f 1534 4218 6129 4578\n",
      "f 958 3067 6129 4218\n",
      "f 282 2655 6130 2658\n",
      "f 658 3618 6130 2655\n",
      "f 1534 4578 6130 3618\n",
      "f 1138 2658 6130 4578\n",
      "f 20 1607 6131 1610\n",
      "f 455 3212 6131 1607\n",
      "f 1535 3218 6131 3212\n",
      "f 458 1610 6131 3218\n",
      "f 167 2198 6132 2195\n",
      "f 908 4117 6132 2198\n",
      "f 1535 3212 6132 4117\n",
      "f 455 2195 6132 3212\n",
      "f 386 3072 6133 3071\n",
      "f 914 4129 6133 3072\n",
      "f 1535 4117 6133 4129\n",
      "f 908 3071 6133 4117\n",
      "f 170 2207 6134 2210\n",
      "f 458 3218 6134 2207\n",
      "f 1535 4129 6134 3218\n",
      "f 914 2210 6134 4129\n",
      "f 47 1718 6135 1716\n",
      "f 566 3434 6135 1718\n",
      "f 1536 3430 6135 3434\n",
      "f 564 1716 6135 3430\n",
      "f 236 2474 6136 2471\n",
      "f 1046 4393 6136 2474\n",
      "f 1536 3434 6136 4393\n",
      "f 566 2471 6136 3434\n",
      "f 386 3071 6137 3073\n",
      "f 908 4118 6137 3071\n",
      "f 1536 4393 6137 4118\n",
      "f 1046 3073 6137 4393\n",
      "f 167 2196 6138 2198\n",
      "f 564 3430 6138 2196\n",
      "f 1536 4118 6138 3430\n",
      "f 908 2198 6138 4118\n",
      "f 98 1922 6139 1920\n",
      "f 770 3842 6139 1922\n",
      "f 1537 3838 6139 3842\n",
      "f 768 1920 6139 3838\n",
      "f 290 2690 6140 2688\n",
      "f 1154 4609 6140 2690\n",
      "f 1537 3842 6140 4609\n",
      "f 770 2688 6140 3842\n",
      "f 386 3073 6141 3074\n",
      "f 1046 4394 6141 3073\n",
      "f 1537 4609 6141 4394\n",
      "f 1154 3074 6141 4609\n",
      "f 236 2472 6142 2474\n",
      "f 768 3838 6142 2472\n",
      "f 1537 4394 6142 3838\n",
      "f 1046 2474 6142 4394\n",
      "f 74 1823 6143 1826\n",
      "f 671 3644 6143 1823\n",
      "f 1538 3650 6143 3644\n",
      "f 674 1826 6143 3650\n",
      "f 170 2210 6144 2208\n",
      "f 914 4130 6144 2210\n",
      "f 1538 3644 6144 4130\n",
      "f 671 2208 6144 3644\n",
      "f 386 3074 6145 3072\n",
      "f 1154 4610 6145 3074\n",
      "f 1538 4130 6145 4610\n",
      "f 914 3072 6145 4130\n",
      "f 290 2687 6146 2690\n",
      "f 674 3650 6146 2687\n",
      "f 1538 4610 6146 3650\n",
      "f 1154 2690 6146 4610\n"
     ]
    }
   ],
   "source": [
    "OBJ_TEMPLATE = '''\n",
    "# {name}\n",
    "{vertices}\n",
    "f 1 1541 4611 1540\n",
    "f 389 3079 4611 1541\n",
    "f 1155 3077 4611 3079\n",
    "f 388 1540 4611 3077\n",
    "f 101 1933 4612 1931\n",
    "f 775 3851 4612 1933\n",
    "f 1155 3079 4612 3851\n",
    "f 389 1931 4612 3079\n",
    "f 291 2691 4613 2692\n",
    "f 773 3847 4613 2691\n",
    "f 1155 3851 4613 3847\n",
    "f 775 2692 4613 3851\n",
    "f 100 1927 4614 1929\n",
    "f 388 3077 4614 1927\n",
    "f 1155 3847 4614 3077\n",
    "f 773 1929 4614 3847\n",
    "f 29 1645 4615 1643\n",
    "f 493 3287 4615 1645\n",
    "f 1156 3283 4615 3287\n",
    "f 491 1643 4615 3283\n",
    "f 199 2325 4616 2323\n",
    "f 971 4243 4616 2325\n",
    "f 1156 3287 4616 4243\n",
    "f 493 2323 4616 3287\n",
    "f 291 2692 4617 2694\n",
    "f 775 3852 4617 2692\n",
    "f 1156 4243 4617 3852\n",
    "f 971 2694 4617 4243\n",
    "f 101 1932 4618 1933\n",
    "f 491 3283 4618 1932\n",
    "f 1156 3852 4618 3283\n",
    "f 775 1933 4618 3852\n",
    "f 75 1827 4619 1828\n",
    "f 675 3651 4619 1827\n",
    "f 1157 3653 4619 3651\n",
    "f 676 1828 4619 3653\n",
    "f 197 2317 4620 2316\n",
    "f 967 4235 4620 2317\n",
    "f 1157 3651 4620 4235\n",
    "f 675 2316 4620 3651\n",
    "f 291 2694 4621 2693\n",
    "f 971 4244 4621 2694\n",
    "f 1157 4235 4621 4244\n",
    "f 967 2693 4621 4235\n",
    "f 199 2324 4622 2325\n",
    "f 676 3653 4622 2324\n",
    "f 1157 4244 4622 3653\n",
    "f 971 2325 4622 4244\n",
    "f 28 1639 4623 1641\n",
    "f 487 3275 4623 1639\n",
    "f 1158 3279 4623 3275\n",
    "f 489 1641 4623 3279\n",
    "f 100 1929 4624 1928\n",
    "f 773 3848 4624 1929\n",
    "f 1158 3275 4624 3848\n",
    "f 487 1928 4624 3275\n",
    "f 291 2693 4625 2691\n",
    "f 967 4236 4625 2693\n",
    "f 1158 3848 4625 4236\n",
    "f 773 2691 4625 3848\n",
    "f 197 2315 4626 2317\n",
    "f 489 3279 4626 2315\n",
    "f 1158 4236 4626 3279\n",
    "f 967 2317 4626 4236\n",
    "f 11 1573 4627 1571\n",
    "f 421 3143 4627 1573\n",
    "f 1159 3139 4627 3143\n",
    "f 419 1571 4627 3139\n",
    "f 133 2061 4628 2059\n",
    "f 839 3979 4628 2061\n",
    "f 1159 3143 4628 3979\n",
    "f 421 2059 4628 3143\n",
    "f 292 2695 4629 2696\n",
    "f 835 3971 4629 2695\n",
    "f 1159 3979 4629 3971\n",
    "f 839 2696 4629 3979\n",
    "f 131 2051 4630 2053\n",
    "f 419 3139 4630 2051\n",
    "f 1159 3971 4630 3139\n",
    "f 835 2053 4630 3971\n",
    "f 55 1749 4631 1747\n",
    "f 597 3495 4631 1749\n",
    "f 1160 3491 4631 3495\n",
    "f 595 1747 4631 3491\n",
    "f 251 2533 4632 2531\n",
    "f 1075 4451 4632 2533\n",
    "f 1160 3495 4632 4451\n",
    "f 597 2531 4632 3495\n",
    "f 292 2696 4633 2698\n",
    "f 839 3980 4633 2696\n",
    "f 1160 4451 4633 3980\n",
    "f 1075 2698 4633 4451\n",
    "f 133 2060 4634 2061\n",
    "f 595 3491 4634 2060\n",
    "f 1160 3980 4634 3491\n",
    "f 839 2061 4634 3980\n",
    "f 75 1828 4635 1830\n",
    "f 676 3654 4635 1828\n",
    "f 1161 3657 4635 3654\n",
    "f 678 1830 4635 3657\n",
    "f 199 2326 4636 2324\n",
    "f 972 4245 4636 2326\n",
    "f 1161 3654 4636 4245\n",
    "f 676 2324 4636 3654\n",
    "f 292 2698 4637 2697\n",
    "f 1075 4452 4637 2698\n",
    "f 1161 4245 4637 4452\n",
    "f 972 2697 4637 4245\n",
    "f 251 2532 4638 2533\n",
    "f 678 3657 4638 2532\n",
    "f 1161 4452 4638 3657\n",
    "f 1075 2533 4638 4452\n",
    "f 29 1644 4639 1645\n",
    "f 492 3285 4639 1644\n",
    "f 1162 3288 4639 3285\n",
    "f 493 1645 4639 3288\n",
    "f 131 2053 4640 2052\n",
    "f 835 3972 4640 2053\n",
    "f 1162 3285 4640 3972\n",
    "f 492 2052 4640 3285\n",
    "f 292 2697 4641 2695\n",
    "f 972 4246 4641 2697\n",
    "f 1162 3972 4641 4246\n",
    "f 835 2695 4641 3972\n",
    "f 199 2323 4642 2326\n",
    "f 493 3288 4642 2323\n",
    "f 1162 4246 4642 3288\n",
    "f 972 2326 4642 4246\n",
    "f 21 1611 4643 1612\n",
    "f 459 3219 4643 1611\n",
    "f 1163 3221 4643 3219\n",
    "f 460 1612 4643 3221\n",
    "f 171 2213 4644 2211\n",
    "f 915 4131 4644 2213\n",
    "f 1163 3219 4644 4131\n",
    "f 459 2211 4644 3219\n",
    "f 293 2700 4645 2699\n",
    "f 917 4135 4645 2700\n",
    "f 1163 4131 4645 4135\n",
    "f 915 2699 4645 4131\n",
    "f 172 2215 4646 2217\n",
    "f 460 3221 4646 2215\n",
    "f 1163 4135 4646 3221\n",
    "f 917 2217 4646 4135\n",
    "f 53 1741 4647 1740\n",
    "f 589 3479 4647 1741\n",
    "f 1164 3477 4647 3479\n",
    "f 588 1740 4647 3477\n",
    "f 247 2517 4648 2515\n",
    "f 1067 4435 4648 2517\n",
    "f 1164 3479 4648 4435\n",
    "f 589 2515 4648 3479\n",
    "f 293 2699 4649 2701\n",
    "f 915 4132 4649 2699\n",
    "f 1164 4435 4649 4132\n",
    "f 1067 2701 4649 4435\n",
    "f 171 2212 4650 2213\n",
    "f 588 3477 4650 2212\n",
    "f 1164 4132 4650 3477\n",
    "f 915 2213 4650 4132\n",
    "f 75 1830 4651 1829\n",
    "f 678 3658 4651 1830\n",
    "f 1165 3655 4651 3658\n",
    "f 677 1829 4651 3655\n",
    "f 251 2534 4652 2532\n",
    "f 1076 4453 4652 2534\n",
    "f 1165 3658 4652 4453\n",
    "f 678 2532 4652 3658\n",
    "f 293 2701 4653 2702\n",
    "f 1067 4436 4653 2701\n",
    "f 1165 4453 4653 4436\n",
    "f 1076 2702 4653 4453\n",
    "f 247 2516 4654 2517\n",
    "f 677 3655 4654 2516\n",
    "f 1165 4436 4654 3655\n",
    "f 1067 2517 4654 4436\n",
    "f 55 1748 4655 1749\n",
    "f 596 3493 4655 1748\n",
    "f 1166 3496 4655 3493\n",
    "f 597 1749 4655 3496\n",
    "f 172 2217 4656 2216\n",
    "f 917 4136 4656 2217\n",
    "f 1166 3493 4656 4136\n",
    "f 596 2216 4656 3493\n",
    "f 293 2702 4657 2700\n",
    "f 1076 4454 4657 2702\n",
    "f 1166 4136 4657 4454\n",
    "f 917 2700 4657 4136\n",
    "f 251 2531 4658 2534\n",
    "f 597 3496 4658 2531\n",
    "f 1166 4454 4658 3496\n",
    "f 1076 2534 4658 4454\n",
    "f 10 1567 4659 1569\n",
    "f 415 3131 4659 1567\n",
    "f 1167 3135 4659 3131\n",
    "f 417 1569 4659 3135\n",
    "f 127 2037 4660 2035\n",
    "f 827 3955 4660 2037\n",
    "f 1167 3131 4660 3955\n",
    "f 415 2035 4660 3131\n",
    "f 294 2704 4661 2703\n",
    "f 831 3963 4661 2704\n",
    "f 1167 3955 4661 3963\n",
    "f 827 2703 4661 3955\n",
    "f 129 2043 4662 2045\n",
    "f 417 3135 4662 2043\n",
    "f 1167 3963 4662 3135\n",
    "f 831 2045 4662 3963\n",
    "f 28 1641 4663 1640\n",
    "f 489 3280 4663 1641\n",
    "f 1168 3277 4663 3280\n",
    "f 488 1640 4663 3277\n",
    "f 197 2318 4664 2315\n",
    "f 968 4237 4664 2318\n",
    "f 1168 3280 4664 4237\n",
    "f 489 2315 4664 3280\n",
    "f 294 2703 4665 2705\n",
    "f 827 3956 4665 2703\n",
    "f 1168 4237 4665 3956\n",
    "f 968 2705 4665 4237\n",
    "f 127 2036 4666 2037\n",
    "f 488 3277 4666 2036\n",
    "f 1168 3956 4666 3277\n",
    "f 827 2037 4666 3956\n",
    "f 75 1829 4667 1827\n",
    "f 677 3656 4667 1829\n",
    "f 1169 3652 4667 3656\n",
    "f 675 1827 4667 3652\n",
    "f 247 2518 4668 2516\n",
    "f 1068 4437 4668 2518\n",
    "f 1169 3656 4668 4437\n",
    "f 677 2516 4668 3656\n",
    "f 294 2705 4669 2706\n",
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    "f 110 1969 4932 1967\n",
    "f 793 3887 4932 1969\n",
    "f 1235 3097 4932 3887\n",
    "f 398 1967 4932 3097\n",
    "f 311 2771 4933 2772\n",
    "f 789 3879 4933 2771\n",
    "f 1235 3887 4933 3879\n",
    "f 793 2772 4933 3887\n",
    "f 108 1959 4934 1961\n",
    "f 396 3093 4934 1959\n",
    "f 1235 3879 4934 3093\n",
    "f 789 1961 4934 3879\n",
    "f 38 1681 4935 1679\n",
    "f 529 3359 4935 1681\n",
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    "f 793 3888 4937 2772\n",
    "f 1236 4315 4937 3888\n",
    "f 1007 2774 4937 4315\n",
    "f 110 1968 4938 1969\n",
    "f 527 3355 4938 1968\n",
    "f 1236 3888 4938 3355\n",
    "f 793 1969 4938 3888\n",
    "f 80 1847 4939 1848\n",
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    "f 213 2381 4940 2380\n",
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    "f 36 1671 4943 1673\n",
    "f 519 3339 4943 1671\n",
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    "f 521 1673 4943 3343\n",
    "f 108 1961 4944 1960\n",
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    "f 311 2773 4945 2771\n",
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    "f 271 2613 4952 2611\n",
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    "f 312 2776 4953 2778\n",
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    "f 80 1848 4955 1850\n",
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    "f 80 1850 4971 1849\n",
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    "f 271 2614 4972 2612\n",
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    "f 137 2075 4982 2078\n",
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    "f 213 2380 4990 2382\n",
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    "f 1000 2382 4990 4302\n",
    "f 57 1755 4991 1758\n",
    "f 603 3508 4991 1755\n",
    "f 1250 3514 4991 3508\n",
    "f 606 1758 4991 3514\n",
    "f 137 2078 4992 2076\n",
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    "f 314 2786 4993 2784\n",
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    "f 1250 4474 4994 3514\n",
    "f 1086 2554 4994 4474\n",
    "f 8 1561 4995 1560\n",
    "f 409 3119 4995 1561\n",
    "f 1251 3117 4995 3119\n",
    "f 408 1560 4995 3117\n",
    "f 121 2013 4996 2011\n",
    "f 815 3931 4996 2013\n",
    "f 1251 3119 4996 3931\n",
    "f 409 2011 4996 3119\n",
    "f 315 2787 4997 2788\n",
    "f 813 3927 4997 2787\n",
    "f 1251 3931 4997 3927\n",
    "f 815 2788 4997 3931\n",
    "f 120 2007 4998 2009\n",
    "f 408 3117 4998 2007\n",
    "f 1251 3927 4998 3117\n",
    "f 813 2009 4998 3927\n",
    "f 49 1725 4999 1723\n",
    "f 573 3447 4999 1725\n",
    "f 1252 3443 4999 3447\n",
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    "f 239 2485 5000 2483\n",
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    "f 315 2788 5001 2790\n",
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    "f 1252 4403 5001 3932\n",
    "f 1051 2790 5001 4403\n",
    "f 121 2012 5002 2013\n",
    "f 571 3443 5002 2012\n",
    "f 1252 3932 5002 3443\n",
    "f 815 2013 5002 3932\n",
    "f 81 1851 5003 1852\n",
    "f 699 3699 5003 1851\n",
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    "f 239 2484 5006 2485\n",
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    "f 48 1719 5007 1721\n",
    "f 567 3435 5007 1719\n",
    "f 1254 3439 5007 3435\n",
    "f 569 1721 5007 3439\n",
    "f 120 2009 5008 2008\n",
    "f 813 3928 5008 2009\n",
    "f 1254 3435 5008 3928\n",
    "f 567 2008 5008 3435\n",
    "f 315 2789 5009 2787\n",
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    "f 19 1605 5011 1604\n",
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    "f 1255 3205 5011 3207\n",
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    "f 71 1813 5015 1811\n",
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    "f 81 1852 5019 1854\n",
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    "f 48 1721 5047 1720\n",
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    "f 6 1554 5059 1556\n",
    "f 402 3105 5059 1554\n",
    "f 1267 3109 5059 3105\n",
    "f 404 1556 5059 3109\n",
    "f 114 1985 5060 1983\n",
    "f 801 3903 5060 1985\n",
    "f 1267 3105 5060 3903\n",
    "f 402 1983 5060 3105\n",
    "f 319 2804 5061 2803\n",
    "f 805 3911 5061 2804\n",
    "f 1267 3903 5061 3911\n",
    "f 801 2803 5061 3903\n",
    "f 116 1991 5062 1993\n",
    "f 404 3109 5062 1991\n",
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    "f 805 1993 5062 3911\n",
    "f 42 1697 5063 1695\n",
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    "f 123 2019 5142 2021\n",
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    "f 112 1977 5328 1976\n",
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    "f 192 2295 6118 2298\n",
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    "f 1531 4217 6118 3262\n",
    "f 958 2298 6118 4217\n",
    "f 74 1826 6119 1824\n",
    "f 674 3649 6119 1826\n",
    "f 1532 3646 6119 3649\n",
    "f 672 1824 6119 3646\n",
    "f 290 2689 6120 2687\n",
    "f 1153 4607 6120 2689\n",
    "f 1532 3649 6120 4607\n",
    "f 674 2687 6120 3649\n",
    "f 385 3068 6121 3070\n",
    "f 962 4226 6121 3068\n",
    "f 1532 4607 6121 4226\n",
    "f 1153 3070 6121 4607\n",
    "f 194 2304 6122 2306\n",
    "f 672 3646 6122 2304\n",
    "f 1532 4226 6122 3646\n",
    "f 962 2306 6122 4226\n",
    "f 98 1921 6123 1922\n",
    "f 769 3840 6123 1921\n",
    "f 1533 3841 6123 3840\n",
    "f 770 1922 6123 3841\n",
    "f 282 2658 6124 2656\n",
    "f 1138 4577 6124 2658\n",
    "f 1533 3840 6124 4577\n",
    "f 769 2656 6124 3840\n",
    "f 385 3070 6125 3069\n",
    "f 1153 4608 6125 3070\n",
    "f 1533 4577 6125 4608\n",
    "f 1138 3069 6125 4577\n",
    "f 290 2688 6126 2689\n",
    "f 770 3841 6126 2688\n",
    "f 1533 4608 6126 3841\n",
    "f 1153 2689 6126 4608\n",
    "f 70 1808 6127 1810\n",
    "f 656 3614 6127 1808\n",
    "f 1534 3618 6127 3614\n",
    "f 658 1810 6127 3618\n",
    "f 192 2298 6128 2296\n",
    "f 958 4218 6128 2298\n",
    "f 1534 3614 6128 4218\n",
    "f 656 2296 6128 3614\n",
    "f 385 3069 6129 3067\n",
    "f 1138 4578 6129 3069\n",
    "f 1534 4218 6129 4578\n",
    "f 958 3067 6129 4218\n",
    "f 282 2655 6130 2658\n",
    "f 658 3618 6130 2655\n",
    "f 1534 4578 6130 3618\n",
    "f 1138 2658 6130 4578\n",
    "f 20 1607 6131 1610\n",
    "f 455 3212 6131 1607\n",
    "f 1535 3218 6131 3212\n",
    "f 458 1610 6131 3218\n",
    "f 167 2198 6132 2195\n",
    "f 908 4117 6132 2198\n",
    "f 1535 3212 6132 4117\n",
    "f 455 2195 6132 3212\n",
    "f 386 3072 6133 3071\n",
    "f 914 4129 6133 3072\n",
    "f 1535 4117 6133 4129\n",
    "f 908 3071 6133 4117\n",
    "f 170 2207 6134 2210\n",
    "f 458 3218 6134 2207\n",
    "f 1535 4129 6134 3218\n",
    "f 914 2210 6134 4129\n",
    "f 47 1718 6135 1716\n",
    "f 566 3434 6135 1718\n",
    "f 1536 3430 6135 3434\n",
    "f 564 1716 6135 3430\n",
    "f 236 2474 6136 2471\n",
    "f 1046 4393 6136 2474\n",
    "f 1536 3434 6136 4393\n",
    "f 566 2471 6136 3434\n",
    "f 386 3071 6137 3073\n",
    "f 908 4118 6137 3071\n",
    "f 1536 4393 6137 4118\n",
    "f 1046 3073 6137 4393\n",
    "f 167 2196 6138 2198\n",
    "f 564 3430 6138 2196\n",
    "f 1536 4118 6138 3430\n",
    "f 908 2198 6138 4118\n",
    "f 98 1922 6139 1920\n",
    "f 770 3842 6139 1922\n",
    "f 1537 3838 6139 3842\n",
    "f 768 1920 6139 3838\n",
    "f 290 2690 6140 2688\n",
    "f 1154 4609 6140 2690\n",
    "f 1537 3842 6140 4609\n",
    "f 770 2688 6140 3842\n",
    "f 386 3073 6141 3074\n",
    "f 1046 4394 6141 3073\n",
    "f 1537 4609 6141 4394\n",
    "f 1154 3074 6141 4609\n",
    "f 236 2472 6142 2474\n",
    "f 768 3838 6142 2472\n",
    "f 1537 4394 6142 3838\n",
    "f 1046 2474 6142 4394\n",
    "f 74 1823 6143 1826\n",
    "f 671 3644 6143 1823\n",
    "f 1538 3650 6143 3644\n",
    "f 674 1826 6143 3650\n",
    "f 170 2210 6144 2208\n",
    "f 914 4130 6144 2210\n",
    "f 1538 3644 6144 4130\n",
    "f 671 2208 6144 3644\n",
    "f 386 3074 6145 3072\n",
    "f 1154 4610 6145 3074\n",
    "f 1538 4130 6145 4610\n",
    "f 914 3072 6145 4130\n",
    "f 290 2687 6146 2690\n",
    "f 674 3650 6146 2687\n",
    "f 1538 4610 6146 3650\n",
    "f 1154 2690 6146 4610\n",
    "'''.strip()\n",
    "\n",
    "def make_obj (verts, name):\n",
    "    return OBJ_TEMPLATE.format(name=name, vertices='\\n'.join([\n",
    "        'v %s %s %s'%(verts[i], verts[i+1], verts[i+2])\n",
    "        for i in range(0, len(verts) - 2, 3)\n",
    "    ]))\n",
    "print(make_obj(x_train[12], sw_keys[12]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading altmodel/altmodel-epoch-40.h5\n",
      "predicting for epoch 40\n",
      "generating write-obj jobs for epoch 40\n",
      "dispatching write-obj jobs for epoch 40\n",
      "writing altencoder-objs/40/input/train-b8c8758f8ee058182a9eb0f146e94477-input.obj\n",
      "writing altencoder-objs/40/output/train-b8c8758f8ee058182a9eb0f146e94477-output.obj\n",
      "writing altencoder-objs/40/input/test-a52f704da0d3c947ca92a4cdad802b45-input.obj\n",
      "writing altencoder-objs/40/output/test-a52f704da0d3c947ca92a4cdad802b45-output.obj\n",
      "writing altencoder-objs/40/input/train-9b0d7aac4500546911718baa6d4afc0b-input.obj\n",
      "writing altencoder-objs/40/output/train-9b0d7aac4500546911718baa6d4afc0b-output.obj\n",
      "writing altencoder-objs/40/input/test-f61888bca4392106bc790478b4a84bf2-input.obj\n",
      "writing altencoder-objs/40/output/test-f61888bca4392106bc790478b4a84bf2-output.obj\n",
      "writing altencoder-objs/40/input/train-86c31c04c436f5932e877e82c90c24d-input.obj\n",
      "writing altencoder-objs/40/output/train-86c31c04c436f5932e877e82c90c24d-output.obj\n",
      "writing altencoder-objs/40/input/test-ed2e4dafc745bdd661fd7e090d4d0d45-input.obj\n",
      "writing altencoder-objs/40/output/test-ed2e4dafc745bdd661fd7e090d4d0d45-output.obj\n",
      "writing altencoder-objs/40/input/train-ba1b8956b1d84856369912a1ddf80ef7-input.obj\n",
      "writing altencoder-objs/40/output/train-ba1b8956b1d84856369912a1ddf80ef7-output.obj\n",
      "writing altencoder-objs/40/input/test-d2e1dc21db9b45df6436916a86a90ed7-input.obj\n",
      "writing altencoder-objs/40/output/test-d2e1dc21db9b45df6436916a86a90ed7-output.obj\n",
      "writing altencoder-objs/40/input/train-909422a9d1b42e1380360680c1602c7d-input.obj\n",
      "writing altencoder-objs/40/output/train-909422a9d1b42e1380360680c1602c7d-output.obj\n",
      "writing altencoder-objs/40/input/test-dd0b595b15a7203e185ce5d54f27f6b9-input.obj\n",
      "writing altencoder-objs/40/output/test-dd0b595b15a7203e185ce5d54f27f6b9-output.obj\n",
      "writing altencoder-objs/40/input/train-15cc37ac9b3b756acfd826dd468a5497-input.obj\n",
      "writing altencoder-objs/40/output/train-15cc37ac9b3b756acfd826dd468a5497-output.obj\n",
      "writing altencoder-objs/40/input/test-8fc3cde1054cc1aaceb4167db4d0e4de-input.obj\n",
      "writing altencoder-objs/40/output/test-8fc3cde1054cc1aaceb4167db4d0e4de-output.obj\n",
      "writing altencoder-objs/40/input/train-180d1e3463c29355bda72093f9b5aa73-input.obj\n",
      "writing altencoder-objs/40/output/train-180d1e3463c29355bda72093f9b5aa73-output.obj\n",
      "writing altencoder-objs/40/input/test-b0a659083ec6e936ca92a4cdad802b45-input.obj\n",
      "writing altencoder-objs/40/output/test-b0a659083ec6e936ca92a4cdad802b45-output.obj\n",
      "writing altencoder-objs/40/input/train-d849e0fee026c98aeece2324fbbe339-input.obj\n",
      "writing altencoder-objs/40/output/train-d849e0fee026c98aeece2324fbbe339-output.obj\n",
      "writing altencoder-objs/40/input/test-862a345b6328d641ef3ecc8551d2c1fa-input.obj\n",
      "writing altencoder-objs/40/output/test-862a345b6328d641ef3ecc8551d2c1fa-output.obj\n",
      "writing altencoder-objs/40/input/train-c61ec8875746ab78227375499b768057-input.obj\n",
      "writing altencoder-objs/40/output/train-c61ec8875746ab78227375499b768057-output.obj\n",
      "writing altencoder-objs/40/input/test-12cf05b9f5f2d2c195c8d3a7db4dbe65-input.obj\n",
      "writing altencoder-objs/40/output/test-12cf05b9f5f2d2c195c8d3a7db4dbe65-output.obj\n",
      "writing altencoder-objs/40/input/train-83f26e6b8298c3a0a134736201a79843-input.obj\n",
      "writing altencoder-objs/40/output/train-83f26e6b8298c3a0a134736201a79843-output.obj\n",
      "writing altencoder-objs/40/input/test-9c4a3879c71df693af0f25977186b501-input.obj\n",
      "writing altencoder-objs/40/output/test-9c4a3879c71df693af0f25977186b501-output.obj\n",
      "done processing models for epoch 40\n",
      "Loading altmodel/altmodel-epoch-30.h5\n",
      "predicting for epoch 30\n",
      "generating write-obj jobs for epoch 30\n",
      "dispatching write-obj jobs for epoch 30\n",
      "writing altencoder-objs/30/input/train-b8c8758f8ee058182a9eb0f146e94477-input.obj\n",
      "writing altencoder-objs/30/output/train-b8c8758f8ee058182a9eb0f146e94477-output.obj\n",
      "writing altencoder-objs/30/input/test-a52f704da0d3c947ca92a4cdad802b45-input.obj\n",
      "writing altencoder-objs/30/output/test-a52f704da0d3c947ca92a4cdad802b45-output.obj\n",
      "writing altencoder-objs/30/input/train-9b0d7aac4500546911718baa6d4afc0b-input.obj\n",
      "writing altencoder-objs/30/output/train-9b0d7aac4500546911718baa6d4afc0b-output.obj\n",
      "writing altencoder-objs/30/input/test-f61888bca4392106bc790478b4a84bf2-input.obj\n",
      "writing altencoder-objs/30/output/test-f61888bca4392106bc790478b4a84bf2-output.obj\n",
      "writing altencoder-objs/30/input/train-86c31c04c436f5932e877e82c90c24d-input.obj\n",
      "writing altencoder-objs/30/output/train-86c31c04c436f5932e877e82c90c24d-output.obj\n",
      "writing altencoder-objs/30/input/test-ed2e4dafc745bdd661fd7e090d4d0d45-input.obj\n",
      "writing altencoder-objs/30/output/test-ed2e4dafc745bdd661fd7e090d4d0d45-output.obj\n",
      "writing altencoder-objs/30/input/train-ba1b8956b1d84856369912a1ddf80ef7-input.obj\n",
      "writing altencoder-objs/30/output/train-ba1b8956b1d84856369912a1ddf80ef7-output.obj\n",
      "writing altencoder-objs/30/input/test-d2e1dc21db9b45df6436916a86a90ed7-input.obj\n",
      "writing altencoder-objs/30/output/test-d2e1dc21db9b45df6436916a86a90ed7-output.obj\n",
      "writing altencoder-objs/30/input/train-909422a9d1b42e1380360680c1602c7d-input.obj\n",
      "writing altencoder-objs/30/output/train-909422a9d1b42e1380360680c1602c7d-output.obj\n",
      "writing altencoder-objs/30/input/test-dd0b595b15a7203e185ce5d54f27f6b9-input.obj\n",
      "writing altencoder-objs/30/output/test-dd0b595b15a7203e185ce5d54f27f6b9-output.obj\n",
      "writing altencoder-objs/30/input/train-15cc37ac9b3b756acfd826dd468a5497-input.obj\n",
      "writing altencoder-objs/30/output/train-15cc37ac9b3b756acfd826dd468a5497-output.obj\n",
      "writing altencoder-objs/30/input/test-8fc3cde1054cc1aaceb4167db4d0e4de-input.obj\n",
      "writing altencoder-objs/30/output/test-8fc3cde1054cc1aaceb4167db4d0e4de-output.obj\n",
      "writing altencoder-objs/30/input/train-180d1e3463c29355bda72093f9b5aa73-input.obj\n",
      "writing altencoder-objs/30/output/train-180d1e3463c29355bda72093f9b5aa73-output.obj\n",
      "writing altencoder-objs/30/input/test-b0a659083ec6e936ca92a4cdad802b45-input.obj\n",
      "writing altencoder-objs/30/output/test-b0a659083ec6e936ca92a4cdad802b45-output.obj\n",
      "writing altencoder-objs/30/input/train-d849e0fee026c98aeece2324fbbe339-input.obj\n",
      "writing altencoder-objs/30/output/train-d849e0fee026c98aeece2324fbbe339-output.obj\n",
      "writing altencoder-objs/30/input/test-862a345b6328d641ef3ecc8551d2c1fa-input.obj\n",
      "writing altencoder-objs/30/output/test-862a345b6328d641ef3ecc8551d2c1fa-output.obj\n",
      "writing altencoder-objs/30/input/train-c61ec8875746ab78227375499b768057-input.obj\n",
      "writing altencoder-objs/30/output/train-c61ec8875746ab78227375499b768057-output.obj\n",
      "writing altencoder-objs/30/input/test-12cf05b9f5f2d2c195c8d3a7db4dbe65-input.obj\n",
      "writing altencoder-objs/30/output/test-12cf05b9f5f2d2c195c8d3a7db4dbe65-output.obj\n",
      "writing altencoder-objs/30/input/train-83f26e6b8298c3a0a134736201a79843-input.obj\n",
      "writing altencoder-objs/30/output/train-83f26e6b8298c3a0a134736201a79843-output.obj\n",
      "writing altencoder-objs/30/input/test-9c4a3879c71df693af0f25977186b501-input.obj\n",
      "writing altencoder-objs/30/output/test-9c4a3879c71df693af0f25977186b501-output.obj\n",
      "done processing models for epoch 30\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Process ForkPoolWorker-105:\n",
      "Process ForkPoolWorker-101:\n",
      "Process ForkPoolWorker-98:\n",
      "Process ForkPoolWorker-99:\n",
      "Process ForkPoolWorker-104:\n",
      "Process ForkPoolWorker-97:\n",
      "Process ForkPoolWorker-108:\n",
      "Process ForkPoolWorker-106:\n",
      "Process ForkPoolWorker-107:\n",
      "Process ForkPoolWorker-102:\n",
      "Process ForkPoolWorker-103:\n",
      "Process ForkPoolWorker-100:\n",
      "Traceback (most recent call last):\n",
      "Traceback (most recent call last):\n",
      "Traceback (most recent call last):\n",
      "Traceback (most recent call last):\n",
      "Traceback (most recent call last):\n",
      "Traceback (most recent call last):\n",
      "Traceback (most recent call last):\n",
      "Traceback (most recent call last):\n",
      "Traceback (most recent call last):\n",
      "Traceback (most recent call last):\n",
      "Traceback (most recent call last):\n",
      "Traceback (most recent call last):\n",
      "  File \"/usr/local/Cellar/python/3.7.2/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/process.py\", line 297, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/usr/local/Cellar/python/3.7.2/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/process.py\", line 297, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/usr/local/Cellar/python/3.7.2/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/process.py\", line 297, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/usr/local/Cellar/python/3.7.2/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/process.py\", line 297, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/usr/local/Cellar/python/3.7.2/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/process.py\", line 297, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/usr/local/Cellar/python/3.7.2/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/process.py\", line 297, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/usr/local/Cellar/python/3.7.2/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/process.py\", line 297, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/usr/local/Cellar/python/3.7.2/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/process.py\", line 297, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/usr/local/Cellar/python/3.7.2/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/process.py\", line 297, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/usr/local/Cellar/python/3.7.2/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/process.py\", line 297, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/usr/local/Cellar/python/3.7.2/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/process.py\", line 297, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/usr/local/Cellar/python/3.7.2/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/process.py\", line 297, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/usr/local/Cellar/python/3.7.2/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/process.py\", line 99, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/usr/local/Cellar/python/3.7.2/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/process.py\", line 99, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/usr/local/Cellar/python/3.7.2/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/process.py\", line 99, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/usr/local/Cellar/python/3.7.2/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/process.py\", line 99, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/usr/local/Cellar/python/3.7.2/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/process.py\", line 99, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/usr/local/Cellar/python/3.7.2/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/process.py\", line 99, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/usr/local/Cellar/python/3.7.2/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/process.py\", line 99, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/usr/local/Cellar/python/3.7.2/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/process.py\", line 99, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/usr/local/Cellar/python/3.7.2/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/process.py\", line 99, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/usr/local/Cellar/python/3.7.2/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/process.py\", line 99, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/usr/local/Cellar/python/3.7.2/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/process.py\", line 99, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/usr/local/Cellar/python/3.7.2/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/process.py\", line 99, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/usr/local/Cellar/python/3.7.2/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/pool.py\", line 110, in worker\n",
      "    task = get()\n",
      "  File \"/usr/local/Cellar/python/3.7.2/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/pool.py\", line 110, in worker\n",
      "    task = get()\n",
      "  File \"/usr/local/Cellar/python/3.7.2/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/pool.py\", line 110, in worker\n",
      "    task = get()\n",
      "  File \"/usr/local/Cellar/python/3.7.2/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/pool.py\", line 110, in worker\n",
      "    task = get()\n",
      "  File \"/usr/local/Cellar/python/3.7.2/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/pool.py\", line 110, in worker\n",
      "    task = get()\n",
      "  File \"/usr/local/Cellar/python/3.7.2/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/pool.py\", line 110, in worker\n",
      "    task = get()\n",
      "  File \"/usr/local/Cellar/python/3.7.2/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/pool.py\", line 110, in worker\n",
      "    task = get()\n",
      "  File \"/usr/local/Cellar/python/3.7.2/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/pool.py\", line 110, in worker\n",
      "    task = get()\n",
      "  File \"/usr/local/Cellar/python/3.7.2/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/pool.py\", line 110, in worker\n",
      "    task = get()\n",
      "  File \"/usr/local/Cellar/python/3.7.2/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/pool.py\", line 110, in worker\n",
      "    task = get()\n",
      "  File \"/usr/local/Cellar/python/3.7.2/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/pool.py\", line 110, in worker\n",
      "    task = get()\n",
      "  File \"/usr/local/Cellar/python/3.7.2/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/pool.py\", line 110, in worker\n",
      "    task = get()\n",
      "  File \"/usr/local/Cellar/python/3.7.2/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/queues.py\", line 351, in get\n",
      "    with self._rlock:\n",
      "  File \"/usr/local/Cellar/python/3.7.2/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/queues.py\", line 351, in get\n",
      "    with self._rlock:\n",
      "  File \"/usr/local/Cellar/python/3.7.2/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/queues.py\", line 351, in get\n",
      "    with self._rlock:\n",
      "  File \"/usr/local/Cellar/python/3.7.2/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/queues.py\", line 351, in get\n",
      "    with self._rlock:\n",
      "  File \"/usr/local/Cellar/python/3.7.2/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/queues.py\", line 351, in get\n",
      "    with self._rlock:\n",
      "  File \"/usr/local/Cellar/python/3.7.2/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/queues.py\", line 351, in get\n",
      "    with self._rlock:\n",
      "  File \"/usr/local/Cellar/python/3.7.2/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/queues.py\", line 351, in get\n",
      "    with self._rlock:\n",
      "  File \"/usr/local/Cellar/python/3.7.2/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/queues.py\", line 351, in get\n",
      "    with self._rlock:\n",
      "  File \"/usr/local/Cellar/python/3.7.2/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/queues.py\", line 351, in get\n",
      "    with self._rlock:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  File \"/usr/local/Cellar/python/3.7.2/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/queues.py\", line 351, in get\n",
      "    with self._rlock:\n",
      "  File \"/usr/local/Cellar/python/3.7.2/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/queues.py\", line 351, in get\n",
      "    with self._rlock:\n",
      "  File \"/usr/local/Cellar/python/3.7.2/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/queues.py\", line 352, in get\n",
      "    res = self._reader.recv_bytes()\n",
      "  File \"/usr/local/Cellar/python/3.7.2/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/synchronize.py\", line 95, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "  File \"/usr/local/Cellar/python/3.7.2/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/synchronize.py\", line 95, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "  File \"/usr/local/Cellar/python/3.7.2/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/synchronize.py\", line 95, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "  File \"/usr/local/Cellar/python/3.7.2/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/synchronize.py\", line 95, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "  File \"/usr/local/Cellar/python/3.7.2/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/synchronize.py\", line 95, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "  File \"/usr/local/Cellar/python/3.7.2/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/synchronize.py\", line 95, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "  File \"/usr/local/Cellar/python/3.7.2/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/synchronize.py\", line 95, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "  File \"/usr/local/Cellar/python/3.7.2/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/synchronize.py\", line 95, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "  File \"/usr/local/Cellar/python/3.7.2/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/synchronize.py\", line 95, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "  File \"/usr/local/Cellar/python/3.7.2/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/synchronize.py\", line 95, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "  File \"/usr/local/Cellar/python/3.7.2/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/synchronize.py\", line 95, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "KeyboardInterrupt\n",
      "  File \"/usr/local/Cellar/python/3.7.2/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/connection.py\", line 216, in recv_bytes\n",
      "    buf = self._recv_bytes(maxlength)\n",
      "KeyboardInterrupt\n",
      "KeyboardInterrupt\n",
      "KeyboardInterrupt\n",
      "KeyboardInterrupt\n",
      "KeyboardInterrupt\n",
      "KeyboardInterrupt\n",
      "KeyboardInterrupt\n",
      "KeyboardInterrupt\n",
      "KeyboardInterrupt\n",
      "KeyboardInterrupt\n",
      "  File \"/usr/local/Cellar/python/3.7.2/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/connection.py\", line 407, in _recv_bytes\n",
      "    buf = self._recv(4)\n",
      "  File \"/usr/local/Cellar/python/3.7.2/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/connection.py\", line 379, in _recv\n",
      "    chunk = read(handle, remaining)\n",
      "KeyboardInterrupt\n"
     ]
    }
   ],
   "source": [
    "import multiprocessing as mp\n",
    "pool = mp.Pool(12)\n",
    "\n",
    "def write_obj (args):\n",
    "    verts, path = args\n",
    "    print(\"writing %s\"%path)\n",
    "    with open(path, 'w') as f:\n",
    "        f.write(make_obj(verts, path))\n",
    "        \n",
    "def build_dirs (paths):\n",
    "    for path in paths:\n",
    "        if not os.path.exists(path):\n",
    "            os.makedirs(path)\n",
    "\n",
    "def export_model_objs (epoch, num_samples=10, output_dir='altencoder-objs', parallel=False):\n",
    "    model = load_model_from_epoch(epoch)\n",
    "    print(\"predicting for epoch %s\"%epoch)\n",
    "    y_train, y_test = map(model.predict, (x_train, x_test))\n",
    "    tasks = []\n",
    "    print(\"generating write-obj jobs for epoch %s\"%epoch)\n",
    "    for i in range(num_samples):\n",
    "        train_name = sw_keys[i].split('/')[1].split('.')[0]\n",
    "        test_name  = sw_keys[i+500].split('/')[1].split('.')[0]\n",
    "        \n",
    "        build_dirs([\n",
    "            os.path.join(output_dir, str(epoch), 'input'),\n",
    "            os.path.join(output_dir, str(epoch), 'output'),\n",
    "        ])\n",
    "        tasks.append((x_train[i], os.path.join(output_dir, str(epoch), 'input', 'train-%s-input.obj'%train_name)))\n",
    "        tasks.append((y_train[i], os.path.join(output_dir, str(epoch), 'output', 'train-%s-output.obj'%train_name)))\n",
    "        tasks.append((x_test[i], os.path.join(output_dir, str(epoch), 'input', 'test-%s-input.obj'%test_name)))\n",
    "        tasks.append((y_test[i], os.path.join(output_dir, str(epoch), 'output', 'test-%s-output.obj'%test_name)))\n",
    "    \n",
    "    print(\"dispatching write-obj jobs for epoch %s\"%epoch)\n",
    "    if parallel:\n",
    "        pool.map(write_obj, tasks)\n",
    "    else:\n",
    "        list(map(write_obj, tasks))\n",
    "    print(\"done processing models for epoch %s\"%epoch)\n",
    "\n",
    "# export_model_objs(2)\n",
    "# export_model_objs(300)\n",
    "# export_model_objs(200)\n",
    "# export_model_objs(100)\n",
    "# export_model_objs(50)\n",
    "export_model_objs(40)\n",
    "export_model_objs(30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train, y_test = model.predict(x_train), model.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.188041,  0.071757, -0.041727, -0.191469,  0.008376,  0.29926 ,\n",
       "       -0.186761,  0.076401, -0.039019, -0.191926,  0.010706, -0.317276,\n",
       "       -0.176773, -0.078195, -0.001716, -0.1874  , -0.107019,  0.266355,\n",
       "       -0.19355 ,  0.027168,  0.255872, -0.193178,  0.020274, -0.28742 ,\n",
       "       -0.1874  , -0.118797, -0.285778,  0.002345, -0.018081, -0.446181,\n",
       "       -0.139854, -0.006102, -0.403974,  0.001501,  0.021966, -0.43595 ,\n",
       "        0.144545, -0.006102, -0.403974,  0.002345, -0.066678, -0.445537,\n",
       "       -0.129798, -0.059359, -0.40573 , -0.140946,  0.028435, -0.388389,\n",
       "        0.145285,  0.028404, -0.388583,  0.132084, -0.059637, -0.406831,\n",
       "        0.192732,  0.071757, -0.041727,  0.196616,  0.010706, -0.317276,\n",
       "        0.191451,  0.076401, -0.039019,  0.19616 ,  0.008376,  0.29926 ,\n",
       "        0.181463, -0.078195, -0.001712,  0.19209 , -0.118797, -0.286042,\n",
       "        0.197869,  0.020274, -0.28742 ,  0.198237,  0.027142,  0.256007,\n",
       "        0.19209 , -0.107019,  0.26609 ,  0.001707,  0.020511,  0.437536,\n",
       "        0.137851,  0.004251,  0.408247,  0.002345,  0.072133,  0.431312,\n",
       "       -0.13686 ,  0.004259,  0.406332,  0.002345, -0.035484,  0.426571,\n",
       "        0.119201, -0.047896,  0.412154,  0.116169,  0.07029 ,  0.405576,\n",
       "       -0.111479,  0.07029 ,  0.405576, -0.11451 , -0.047896,  0.412154,\n",
       "       -0.      , -0.079406, -0.013809, -0.138883, -0.123187, -0.28625 ,\n",
       "        0.178353, -0.080933, -0.00401 , -0.138883, -0.114234,  0.253518,\n",
       "       -0.173662, -0.080933, -0.004008, -0.183861, -0.123187, -0.28625 ,\n",
       "        0.188552, -0.123187, -0.286515,  0.188552, -0.114234,  0.253253,\n",
       "       -0.183861, -0.114234,  0.253518, -0.030519,  0.130658,  0.036033,\n",
       "        0.002345,  0.070903, -0.276893, -0.109772,  0.124245,  0.031355,\n",
       "        0.002345,  0.12185 ,  0.193754,  0.114445,  0.124264,  0.031634,\n",
       "        0.165479,  0.052789, -0.262263, -0.160789,  0.052796, -0.262173,\n",
       "       -0.101655,  0.116738,  0.190779,  0.106345,  0.116738,  0.190779,\n",
       "       -0.183861, -0.123187, -0.28625 , -0.175661, -0.080082, -0.00318 ,\n",
       "       -0.183861, -0.114234,  0.253518, -0.183861, -0.086014,  0.302415,\n",
       "       -0.153931,  0.004705,  0.389794, -0.141717,  0.056613,  0.387792,\n",
       "       -0.153769,  0.072766,  0.277219, -0.185846,  0.077788, -0.038483,\n",
       "       -0.160856,  0.053089, -0.25876 , -0.149822,  0.030687, -0.379899,\n",
       "       -0.187486, -0.024256, -0.347802, -0.183861, -0.094951, -0.335138,\n",
       "        0.188552, -0.114265, -0.31396 ,  0.002345, -0.071818, -0.43928 ,\n",
       "       -0.183861, -0.114265, -0.313695, -0.143117,  0.035175, -0.373659,\n",
       "        0.002345,  0.040783, -0.40102 ,  0.147808,  0.035175, -0.373659,\n",
       "        0.154513,  0.030687, -0.379899,  0.192177, -0.024256, -0.347802,\n",
       "        0.188552, -0.094951, -0.335403,  0.188552, -0.114234,  0.253253,\n",
       "        0.180351, -0.080082, -0.003179,  0.188552, -0.123187, -0.286515,\n",
       "        0.165547,  0.053089, -0.25876 ,  0.190536,  0.077788, -0.038483,\n",
       "        0.15843 ,  0.072773,  0.277584,  0.146408,  0.056613,  0.387792,\n",
       "        0.158622,  0.004705,  0.389794,  0.188552, -0.086014,  0.30215 ,\n",
       "       -0.183861, -0.105321,  0.280965, -0.01808 , -0.052722,  0.40967 ,\n",
       "        0.188552, -0.105321,  0.2807  ,  0.112311,  0.073068,  0.403818,\n",
       "        0.002345,  0.074961,  0.428017, -0.109975,  0.072869,  0.402065,\n",
       "       -0.183861, -0.105321,  0.280965, -0.13768 ,  0.068677,  0.380554,\n",
       "       -0.183861, -0.114265, -0.313695, -0.15657 ,  0.040311, -0.347674,\n",
       "        0.188552, -0.105321,  0.2807  ,  0.142371,  0.068677,  0.380554,\n",
       "        0.188552, -0.114265, -0.31396 ,  0.161261,  0.040311, -0.347674])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1.90569937e-01,  2.16046330e-02, -2.42814086e-02, -1.83027416e-01,\n",
       "       -5.75248152e-04,  3.04515243e-01, -1.83217198e-01,  6.07237145e-02,\n",
       "       -3.82540114e-02, -1.83499277e-01, -1.13823311e-02, -3.08607608e-01,\n",
       "       -1.74083784e-01, -6.93430454e-02, -3.85557255e-03, -1.81659818e-01,\n",
       "       -9.26676616e-02,  2.62888491e-01, -1.77214965e-01,  5.37756905e-02,\n",
       "        2.89658010e-01, -1.78229228e-01,  3.21672484e-02, -2.69728482e-01,\n",
       "       -1.81090280e-01, -9.46186632e-02, -2.68231452e-01,  3.77157331e-03,\n",
       "       -2.22427957e-02, -4.29328889e-01, -1.35522544e-01, -2.63939966e-02,\n",
       "       -4.06195939e-01, -5.12617640e-04,  4.54471121e-03, -4.19039786e-01,\n",
       "        1.32203668e-01, -2.92866174e-02, -4.10046130e-01,  1.58825703e-03,\n",
       "       -6.69832081e-02, -4.21616077e-01, -1.27337530e-01, -6.67602643e-02,\n",
       "       -4.04059321e-01, -1.36281550e-01, -8.13290011e-04, -3.94701898e-01,\n",
       "        1.35797545e-01,  4.93134093e-03, -3.95223409e-01,  1.30829513e-01,\n",
       "       -6.47926629e-02, -4.07911420e-01,  1.89748287e-01,  2.24832334e-02,\n",
       "       -3.00936736e-02,  1.88733622e-01, -7.21360371e-03, -2.99545795e-01,\n",
       "        1.85802177e-01,  6.18798211e-02, -3.95606384e-02,  1.86052516e-01,\n",
       "       -3.69463116e-04,  3.05189848e-01,  1.76283240e-01, -7.31266961e-02,\n",
       "       -1.20681636e-02,  1.83994919e-01, -9.18290243e-02, -2.64804929e-01,\n",
       "        1.86849222e-01,  2.59705745e-02, -2.71114945e-01,  1.80007786e-01,\n",
       "        5.14383540e-02,  2.90307611e-01,  1.85560942e-01, -9.50875729e-02,\n",
       "        2.65156686e-01, -7.41815381e-03,  2.43389048e-04,  4.21209395e-01,\n",
       "        1.45730406e-01,  1.85029954e-03,  4.04973090e-01, -3.96796735e-03,\n",
       "        7.25987703e-02,  4.09256101e-01, -1.42065346e-01,  8.77492130e-04,\n",
       "        4.02380884e-01, -1.98469125e-03, -5.50434925e-02,  4.09896731e-01,\n",
       "        1.37411773e-01, -5.34951314e-02,  3.95802945e-01,  1.38727918e-01,\n",
       "        7.35930651e-02,  3.96024346e-01, -1.39644042e-01,  7.41339326e-02,\n",
       "        3.96465987e-01, -1.34514183e-01, -5.27277552e-02,  3.95895779e-01,\n",
       "        3.51861678e-03, -7.63533264e-02, -1.04392879e-02, -5.85044920e-03,\n",
       "       -9.68048424e-02, -2.73534536e-01,  1.70462057e-01, -8.03478509e-02,\n",
       "       -1.01215579e-02, -3.69772427e-02, -1.00301802e-01,  2.63442457e-01,\n",
       "       -1.67297080e-01, -8.05289745e-02, -1.34731457e-02, -1.72575191e-01,\n",
       "       -1.00086711e-01, -2.55825639e-01,  1.77416846e-01, -1.01507075e-01,\n",
       "       -2.55771995e-01,  1.78404436e-01, -1.00469656e-01,  2.53004283e-01,\n",
       "       -1.74947888e-01, -9.90119278e-02,  2.52596676e-01, -8.52947123e-04,\n",
       "        1.29328877e-01,  2.46557016e-02, -4.44662943e-03,  7.82495663e-02,\n",
       "       -1.85622320e-01, -1.01650946e-01,  1.20962918e-01,  3.17645520e-02,\n",
       "       -1.02294376e-03,  1.10878453e-01,  2.39073515e-01,  1.05021536e-01,\n",
       "        1.20950490e-01,  2.94912346e-02,  1.54059529e-01,  5.70200868e-02,\n",
       "       -2.17190444e-01, -1.50111794e-01,  5.95310219e-02, -2.20794991e-01,\n",
       "       -1.27015218e-01,  9.94460136e-02,  2.43606746e-01,  1.26001403e-01,\n",
       "        1.02971107e-01,  2.49794215e-01, -1.77249700e-01, -9.94428694e-02,\n",
       "       -2.52445877e-01, -1.72170684e-01, -8.16790909e-02, -1.84037648e-02,\n",
       "       -1.77945703e-01, -9.97208431e-02,  2.52078652e-01, -1.67956263e-01,\n",
       "       -5.90812080e-02,  3.60402286e-01, -1.66262642e-01,  2.39650346e-03,\n",
       "        3.87880951e-01, -1.56906605e-01,  6.67805374e-02,  3.81301671e-01,\n",
       "       -1.56067386e-01,  8.36220533e-02,  2.60273337e-01, -1.67365670e-01,\n",
       "        8.85880291e-02, -2.72452421e-02, -1.72042608e-01,  5.05104586e-02,\n",
       "       -2.24798173e-01, -1.63400248e-01,  1.47109013e-02, -3.57257277e-01,\n",
       "       -1.66656867e-01, -2.33047642e-02, -3.74819160e-01, -1.60807177e-01,\n",
       "       -6.66287318e-02, -3.74163747e-01,  1.46879509e-01, -7.57013932e-02,\n",
       "       -3.86226833e-01, -1.96072366e-03, -7.21489713e-02, -4.20378417e-01,\n",
       "       -1.42605141e-01, -7.58526176e-02, -3.86551350e-01, -1.52989015e-01,\n",
       "        3.16066109e-02, -3.40790302e-01, -1.09812617e-03,  2.71620583e-02,\n",
       "       -3.74692738e-01,  1.51901573e-01,  3.23369391e-02, -3.44762474e-01,\n",
       "        1.70170650e-01,  1.58670116e-02, -3.51478517e-01,  1.68208376e-01,\n",
       "       -2.43107397e-02, -3.76066238e-01,  1.65650174e-01, -6.70867786e-02,\n",
       "       -3.73083025e-01,  1.82813391e-01, -9.95176435e-02,  2.50503808e-01,\n",
       "        1.74037516e-01, -7.76710734e-02, -2.71955617e-02,  1.79404035e-01,\n",
       "       -9.82166678e-02, -2.55080789e-01,  1.72184825e-01,  4.88232188e-02,\n",
       "       -2.22907767e-01,  1.66584700e-01,  8.66082609e-02, -2.90511809e-02,\n",
       "        1.61403239e-01,  8.42851847e-02,  2.60974139e-01,  1.58239707e-01,\n",
       "        6.42204881e-02,  3.81781310e-01,  1.70276210e-01,  1.78979337e-03,\n",
       "        3.86558771e-01,  1.73086494e-01, -5.99440709e-02,  3.56690764e-01,\n",
       "       -1.48749903e-01, -7.22739995e-02,  3.59459281e-01, -5.00336662e-03,\n",
       "       -5.93420379e-02,  4.04736221e-01,  1.55092344e-01, -6.92075714e-02,\n",
       "        3.59939277e-01,  1.40829116e-01,  7.44962245e-02,  3.90784442e-01,\n",
       "        2.12564133e-03,  7.46225864e-02,  4.06410336e-01, -1.39903009e-01,\n",
       "        7.31967464e-02,  3.92510355e-01, -1.79628626e-01, -8.61624256e-02,\n",
       "        2.98011988e-01, -1.52217612e-01,  6.77168742e-02,  3.74174953e-01,\n",
       "       -1.69327363e-01, -8.55343267e-02, -3.31722170e-01, -1.66259930e-01,\n",
       "        3.78276631e-02, -3.23181629e-01,  1.79944590e-01, -8.45502764e-02,\n",
       "        2.97651500e-01,  1.56036928e-01,  6.96540028e-02,  3.74160737e-01,\n",
       "        1.74117118e-01, -8.36487114e-02, -3.30611706e-01,  1.68222353e-01,\n",
       "        3.90516929e-02, -3.23364794e-01], dtype=float32)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/site-packages/ipykernel_launcher.py:2: RuntimeWarning: divide by zero encountered in true_divide\n",
      "  \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([-1.34488608e-02,  6.98919507e-01,  4.18088800e-01,  4.40885132e-02,\n",
       "        1.06867815e+00, -1.75607919e-02,  1.89750662e-02,  2.05197386e-01,\n",
       "        1.96055403e-02,  4.39061063e-02,  2.06317309e+00,  2.73212973e-02,\n",
       "        1.52128197e-02,  1.13203589e-01, -1.24683715e+00,  3.06306420e-02,\n",
       "        1.34100846e-01,  1.30146190e-02,  8.43969764e-02, -9.79376122e-01,\n",
       "       -1.32042623e-01,  7.73834098e-02, -5.86625650e-01,  6.15528433e-02,\n",
       "        3.36697950e-02,  2.03526493e-01,  6.13992277e-02, -6.08346825e-01,\n",
       "       -2.30175083e-01,  3.77696750e-02,  3.09712673e-02, -3.32546649e+00,\n",
       "       -5.50020196e-03,  1.34151742e+00,  7.93102467e-01,  3.87893431e-02,\n",
       "        8.53805503e-02, -3.79951121e+00, -1.50309931e-02,  3.22704891e-01,\n",
       "       -4.57734276e-03,  5.36900921e-02,  1.89561453e-02, -1.24686473e-01,\n",
       "        4.11771205e-03,  3.30938804e-02,  1.02860172e+00, -1.62540600e-02,\n",
       "        6.53023690e-02,  8.26385688e-01, -1.70887788e-02,  9.49764489e-03,\n",
       "       -8.64507413e-02, -2.65569701e-03,  1.54811489e-02,  6.86675399e-01,\n",
       "        2.78796136e-01,  4.00902143e-02,  1.67379074e+00,  5.58825920e-02,\n",
       "        2.95053220e-02,  1.90065299e-01, -1.38814006e-02,  5.15267328e-02,\n",
       "        1.04410973e+00, -1.98150369e-02,  2.85444398e-02,  6.48162152e-02,\n",
       "       -6.04916098e+00,  4.21421258e-02,  2.27008895e-01,  7.42445882e-02,\n",
       "        5.56922935e-02, -2.80979307e-01,  5.67290188e-02,  9.19566703e-02,\n",
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       "        3.50751135e-03,  5.34572572e+00,  9.88133731e-01,  3.73148840e-02,\n",
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       "       -6.45710369e-03,  5.11367626e-02, -3.80340950e-02,  7.93967568e-01,\n",
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       "       -1.52773661e-01, -1.16901859e-01,  3.96721980e-02, -1.94190518e-01,\n",
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       "        2.24619135e-02, -1.74693763e-01, -1.00880139e-01,  3.94469564e-02,\n",
       "                   inf,  3.84438652e-02,  2.44022893e-01,  9.57874980e-01,\n",
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       "        1.87522130e-01,  1.06285978e-01,  5.90561428e-02,  1.75991987e-01,\n",
       "        1.07299811e-01,  5.38183869e-02,  1.20492530e-01,  9.82090191e-04,\n",
       "        4.84774495e-02,  1.33253429e-01,  3.63415478e-03,  9.72051931e-01,\n",
       "        1.01725364e-02,  3.15746632e-01,  2.89621724e+00, -1.03614322e-01,\n",
       "        3.29624369e-01,  7.39811077e-02,  2.64162115e-02, -1.30617763e-02,\n",
       "        1.43622335e+00,  9.00414226e-02, -2.33902345e-01,  8.23405448e-02,\n",
       "        2.66650821e-02,  6.77361519e-02,  6.90085793e-02, -8.01509177e-02,\n",
       "        1.71860139e-01,  6.64050745e-02, -1.27566896e-01,  1.57827119e-01,\n",
       "       -2.49473397e-01,  1.48126458e-01, -2.76905457e-01, -1.84836172e-01,\n",
       "        1.17929836e-01, -3.09338108e-01,  3.59581432e-02,  1.92748671e-01,\n",
       "        1.18093008e-01,  1.98696140e-02, -1.99431939e-02, -4.78734743e+00,\n",
       "        3.21726561e-02,  1.27047612e-01,  5.67749674e-03,  8.65041372e-02,\n",
       "        3.13121027e-01, -1.91747387e-01, -8.01114886e-02,  4.90647510e-01,\n",
       "        4.90784577e-03, -1.07182658e-01, -1.79597219e-01,  1.67366236e-02,\n",
       "       -1.49470067e-02, -1.49191288e-01,  6.11273493e-02,  9.94389430e-02,\n",
       "       -1.38839270e-01,  2.92018758e-01, -6.95442399e-02,  4.85701624e-02,\n",
       "        1.31248366e-01, -9.06291979e-02,  5.20614550e-01,  5.95993230e-02,\n",
       "        1.11097006e-01,  3.92165163e-02, -7.76797129e-02,  1.25387236e-01,\n",
       "        2.98282990e-01, -1.16446798e-01,  2.21013253e-01,  3.37492730e-01,\n",
       "       -2.30178471e-01,  1.83612949e+00, -4.60847224e-03,  4.30285541e-02,\n",
       "        2.24386135e-01,  3.36169276e-01, -2.32252189e-01, -6.89786327e-02,\n",
       "        1.01446742e-01,  8.79644233e-02,  1.46828408e+00,  3.33985772e-01,\n",
       "        6.56507455e-02, -2.76952056e-02,  8.06840334e-02,  7.73339481e-02,\n",
       "       -1.01335486e-01,  4.82940280e-01,  7.48106285e-02,  1.24721608e-01,\n",
       "       -2.25674930e-03, -8.12653110e-02,  1.21461591e-01,  2.93458957e-01,\n",
       "       -1.12342541e-01,  3.04351527e-02,  1.28826414e-01,  1.08555150e-02,\n",
       "        3.50066475e-02,  3.01057244e-02, -7.55475359e+00,  4.85169332e-02,\n",
       "        2.02702657e-01,  1.09712269e-01, -4.00963167e-02,  8.03515080e-02,\n",
       "        1.38554001e-01,  1.25704852e-01, -1.13388452e-01,  2.45090535e-01,\n",
       "       -1.87668925e-02, -1.58193076e-01,  5.98372421e-02, -8.08132571e-02,\n",
       "       -1.34377052e-01,  1.54997786e-02, -7.34715847e-02,  6.19597583e-01,\n",
       "        8.29984265e-03,  8.20224967e-02,  3.03089371e-01, -1.80508901e-01,\n",
       "        1.90965442e-01,  3.13774086e-01, -2.79373876e-01,  7.23265121e-01,\n",
       "       -1.25564999e-01,  1.20433011e-02,  1.77455855e-01,  3.42889154e-01,\n",
       "       -2.82291689e-01, -2.53920953e-01, -1.95465118e-02,  3.22758212e-02,\n",
       "        9.35431424e-02,  4.51452930e-03,  5.04808536e-02, -2.72134657e-01,\n",
       "       -4.49774814e-03,  2.37639324e-02,  2.30194249e-02,  1.81906499e-01,\n",
       "       -6.06729963e-02, -1.05589858e-01,  1.39803110e-02,  1.67625291e-02,\n",
       "        7.90468703e-02,  2.51438964e-01, -5.74671898e-02, -6.18888008e-02,\n",
       "        6.16044473e-02,  7.04463688e-02,  4.56500608e-02,  1.97213505e-01,\n",
       "       -6.03900944e-02, -9.59881457e-02, -1.42260551e-02,  1.67998844e-02,\n",
       "        7.65565037e-02,  2.67941089e-01, -5.30376665e-02, -4.31682357e-02,\n",
       "        3.12397873e-02,  6.99195388e-02])"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "delta = x_train[0] - y_train[0]\n",
    "delta / x_train[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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