NLP/WordVector/word2vecDemo.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.layers import Input, Dense, Embedding\n",
    "\n",
    "from sklearn.preprocessing import OneHotEncoder,LabelEncoder\n",
    "\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('king', 'a'),\n",
       " ('queen', 'a'),\n",
       " ('queen', 'b'),\n",
       " ('king', 'b'),\n",
       " ('king', 'c'),\n",
       " ('queen', 'c'),\n",
       " ('man', 'd'),\n",
       " ('women', 'd'),\n",
       " ('women', 'e'),\n",
       " ('man', 'e'),\n",
       " ('man', 'f'),\n",
       " ('women', 'f'),\n",
       " ('king', 'x'),\n",
       " ('man', 'x'),\n",
       " ('queen', 'y'),\n",
       " ('women', 'y')]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# prepare data\n",
    "king_queen = ['king', 'queen']\n",
    "kq_symbol = ['a', 'b', 'c']\n",
    "man_women = ['man', 'women']\n",
    "wm_symbol = ['d', 'e', 'f']\n",
    "symbol = list(zip(king_queen * 3, kq_symbol * 2)) + list(zip(man_women * 3, wm_symbol *2))\n",
    "symbol.extend([('king', 'x'), ('man', 'x'), ('queen', 'y'), ('women', 'y')])\n",
    "symbol = sorted(symbol, key=lambda x: x[1])\n",
    "symbol"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data, train_label = zip(*symbol)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('a',\n",
       " 'a',\n",
       " 'b',\n",
       " 'b',\n",
       " 'c',\n",
       " 'c',\n",
       " 'd',\n",
       " 'd',\n",
       " 'e',\n",
       " 'e',\n",
       " 'f',\n",
       " 'f',\n",
       " 'x',\n",
       " 'x',\n",
       " 'y',\n",
       " 'y')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('king',\n",
       " 'queen',\n",
       " 'queen',\n",
       " 'king',\n",
       " 'king',\n",
       " 'queen',\n",
       " 'man',\n",
       " 'women',\n",
       " 'women',\n",
       " 'man',\n",
       " 'man',\n",
       " 'women',\n",
       " 'king',\n",
       " 'man',\n",
       " 'queen',\n",
       " 'women')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "vocabulary = list(set(train_data) | set(train_label))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['a', 'b', 'c', 'd', 'e', 'f', 'king', 'man', 'queen', 'women', 'x', 'y']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vocabulary.sort()\n",
    "vocabulary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def one_hot_encoder(word, vocab=vocabulary):\n",
    "    return [1 if word == w else 0 for w in vocab]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "one_hot_encoder('king')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data_encoded = np.array([one_hot_encoder(w) for w in train_data])\n",
    "train_label_encoded = np.array([one_hot_encoder(l) for l in train_label])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0],\n",
       "       [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0],\n",
       "       [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0],\n",
       "       [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data_encoded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# build model\n",
    "Dim = 5\n",
    "V = len(vocabulary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\mingming.xu\\AppData\\Local\\Continuum\\anaconda2\\envs\\py3\\lib\\site-packages\\tensorflow\\python\\ops\\resource_variable_ops.py:435: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Colocations handled automatically by placer.\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input (InputLayer)           (None, 12)                0         \n",
      "_________________________________________________________________\n",
      "embedding (Dense)            (None, 5)                 60        \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 12)                60        \n",
      "=================================================================\n",
      "Total params: 120\n",
      "Trainable params: 120\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "inputs = Input(shape=(V,), name='input')\n",
    "x = Dense(Dim, activation='linear', use_bias=False, name='embedding')(inputs)\n",
    "predict = Dense(V, activation='softmax', use_bias=False, name='dense')(x)\n",
    "model = Model(inputs=inputs, outputs=predict)\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1000\n",
      "16/16 [==============================] - 0s 7ms/sample - loss: 1.6980 - mean_squared_error: 0.0744 - acc: 0.0625\n",
      "Epoch 2/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.5239 - mean_squared_error: 0.0676 - acc: 0.1875\n",
      "Epoch 3/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4929 - mean_squared_error: 0.0664 - acc: 0.1250\n",
      "Epoch 4/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4766 - mean_squared_error: 0.0658 - acc: 0.0000e+00\n",
      "Epoch 5/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4663 - mean_squared_error: 0.0654 - acc: 0.2500\n",
      "Epoch 6/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4596 - mean_squared_error: 0.0651 - acc: 0.0625\n",
      "Epoch 7/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4541 - mean_squared_error: 0.0649 - acc: 0.1250\n",
      "Epoch 8/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4502 - mean_squared_error: 0.0648 - acc: 0.1250\n",
      "Epoch 9/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4469 - mean_squared_error: 0.0646 - acc: 0.0625\n",
      "Epoch 10/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4441 - mean_squared_error: 0.0645 - acc: 0.0000e+00\n",
      "Epoch 11/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4413 - mean_squared_error: 0.0644 - acc: 0.0625\n",
      "Epoch 12/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4391 - mean_squared_error: 0.0643 - acc: 0.0625\n",
      "Epoch 13/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4374 - mean_squared_error: 0.0642 - acc: 0.0625\n",
      "Epoch 14/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4359 - mean_squared_error: 0.0642 - acc: 0.0625\n",
      "Epoch 15/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4343 - mean_squared_error: 0.0641 - acc: 0.0625\n",
      "Epoch 16/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4331 - mean_squared_error: 0.0641 - acc: 0.0000e+00\n",
      "Epoch 17/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4319 - mean_squared_error: 0.0640 - acc: 0.0000e+00\n",
      "Epoch 18/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4309 - mean_squared_error: 0.0640 - acc: 0.0000e+00\n",
      "Epoch 19/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4297 - mean_squared_error: 0.0639 - acc: 0.0625\n",
      "Epoch 20/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4288 - mean_squared_error: 0.0639 - acc: 0.0000e+00\n",
      "Epoch 21/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4279 - mean_squared_error: 0.0639 - acc: 0.1250\n",
      "Epoch 22/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4272 - mean_squared_error: 0.0638 - acc: 0.0000e+00\n",
      "Epoch 23/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4264 - mean_squared_error: 0.0638 - acc: 0.0625\n",
      "Epoch 24/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4258 - mean_squared_error: 0.0638 - acc: 0.0625\n",
      "Epoch 25/1000\n",
      "16/16 [==============================] - 0s 2ms/sample - loss: 1.4250 - mean_squared_error: 0.0638 - acc: 0.1250\n",
      "Epoch 26/1000\n",
      "16/16 [==============================] - 0s 2ms/sample - loss: 1.4245 - mean_squared_error: 0.0637 - acc: 0.0625\n",
      "Epoch 27/1000\n",
      "16/16 [==============================] - 0s 2ms/sample - loss: 1.4238 - mean_squared_error: 0.0637 - acc: 0.0625\n",
      "Epoch 28/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4233 - mean_squared_error: 0.0637 - acc: 0.0625\n",
      "Epoch 29/1000\n",
      "16/16 [==============================] - 0s 2ms/sample - loss: 1.4228 - mean_squared_error: 0.0637 - acc: 0.0625\n",
      "Epoch 30/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4224 - mean_squared_error: 0.0637 - acc: 0.0000e+00\n",
      "Epoch 31/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4218 - mean_squared_error: 0.0636 - acc: 0.0000e+00\n",
      "Epoch 32/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4214 - mean_squared_error: 0.0636 - acc: 0.1250\n",
      "Epoch 33/1000\n",
      "16/16 [==============================] - 0s 2ms/sample - loss: 1.4210 - mean_squared_error: 0.0636 - acc: 0.0625\n",
      "Epoch 34/1000\n",
      "16/16 [==============================] - 0s 2ms/sample - loss: 1.4206 - mean_squared_error: 0.0636 - acc: 0.1250\n",
      "Epoch 35/1000\n",
      "16/16 [==============================] - 0s 2ms/sample - loss: 1.4201 - mean_squared_error: 0.0636 - acc: 0.0000e+00\n",
      "Epoch 36/1000\n",
      "16/16 [==============================] - 0s 2ms/sample - loss: 1.4197 - mean_squared_error: 0.0636 - acc: 0.1250\n",
      "Epoch 37/1000\n",
      "16/16 [==============================] - 0s 2ms/sample - loss: 1.4193 - mean_squared_error: 0.0635 - acc: 0.0000e+00\n",
      "Epoch 38/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4190 - mean_squared_error: 0.0635 - acc: 0.0000e+00\n",
      "Epoch 39/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4187 - mean_squared_error: 0.0635 - acc: 0.0625\n",
      "Epoch 40/1000\n",
      "16/16 [==============================] - 0s 2ms/sample - loss: 1.4183 - mean_squared_error: 0.0635 - acc: 0.0625\n",
      "Epoch 41/1000\n",
      "16/16 [==============================] - 0s 2ms/sample - loss: 1.4180 - mean_squared_error: 0.0635 - acc: 0.0625\n",
      "Epoch 42/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4178 - mean_squared_error: 0.0635 - acc: 0.0625\n",
      "Epoch 43/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4174 - mean_squared_error: 0.0635 - acc: 0.0000e+00\n",
      "Epoch 44/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4172 - mean_squared_error: 0.0635 - acc: 0.1250\n",
      "Epoch 45/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4169 - mean_squared_error: 0.0634 - acc: 0.0625\n",
      "Epoch 46/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4166 - mean_squared_error: 0.0634 - acc: 0.1250\n",
      "Epoch 47/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4163 - mean_squared_error: 0.0634 - acc: 0.0625\n",
      "Epoch 48/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4162 - mean_squared_error: 0.0634 - acc: 0.0000e+00\n",
      "Epoch 49/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4159 - mean_squared_error: 0.0634 - acc: 0.0625\n",
      "Epoch 50/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4156 - mean_squared_error: 0.0634 - acc: 0.0625\n",
      "Epoch 51/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4154 - mean_squared_error: 0.0634 - acc: 0.0625\n",
      "Epoch 52/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4152 - mean_squared_error: 0.0634 - acc: 0.1875\n",
      "Epoch 53/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4150 - mean_squared_error: 0.0634 - acc: 0.0625\n",
      "Epoch 54/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4148 - mean_squared_error: 0.0634 - acc: 0.0625\n",
      "Epoch 55/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4146 - mean_squared_error: 0.0634 - acc: 0.0625\n",
      "Epoch 56/1000\n",
      "16/16 [==============================] - 0s 2ms/sample - loss: 1.4143 - mean_squared_error: 0.0634 - acc: 0.1250\n",
      "Epoch 57/1000\n",
      "16/16 [==============================] - 0s 2ms/sample - loss: 1.4142 - mean_squared_error: 0.0633 - acc: 0.0000e+00\n",
      "Epoch 58/1000\n",
      "16/16 [==============================] - 0s 2ms/sample - loss: 1.4140 - mean_squared_error: 0.0633 - acc: 0.0625\n",
      "Epoch 59/1000\n",
      "16/16 [==============================] - 0s 2ms/sample - loss: 1.4138 - mean_squared_error: 0.0633 - acc: 0.0625\n",
      "Epoch 60/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4136 - mean_squared_error: 0.0633 - acc: 0.0625\n",
      "Epoch 61/1000\n",
      "16/16 [==============================] - 0s 2ms/sample - loss: 1.4134 - mean_squared_error: 0.0633 - acc: 0.0625\n",
      "Epoch 62/1000\n",
      "16/16 [==============================] - 0s 2ms/sample - loss: 1.4133 - mean_squared_error: 0.0633 - acc: 0.1250\n",
      "Epoch 63/1000\n",
      "16/16 [==============================] - 0s 2ms/sample - loss: 1.4131 - mean_squared_error: 0.0633 - acc: 0.1250\n",
      "Epoch 64/1000\n",
      "16/16 [==============================] - 0s 2ms/sample - loss: 1.4130 - mean_squared_error: 0.0633 - acc: 0.0000e+00\n",
      "Epoch 65/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16/16 [==============================] - 0s 2ms/sample - loss: 1.4128 - mean_squared_error: 0.0633 - acc: 0.0000e+00\n",
      "Epoch 66/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4126 - mean_squared_error: 0.0633 - acc: 0.0000e+00\n",
      "Epoch 67/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4125 - mean_squared_error: 0.0633 - acc: 0.1250\n",
      "Epoch 68/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4123 - mean_squared_error: 0.0633 - acc: 0.0625\n",
      "Epoch 69/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4122 - mean_squared_error: 0.0633 - acc: 0.0000e+00\n",
      "Epoch 70/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4120 - mean_squared_error: 0.0633 - acc: 0.1250\n",
      "Epoch 71/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4119 - mean_squared_error: 0.0633 - acc: 0.0625\n",
      "Epoch 72/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4117 - mean_squared_error: 0.0633 - acc: 0.0625\n",
      "Epoch 73/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4116 - mean_squared_error: 0.0633 - acc: 0.0625\n",
      "Epoch 74/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4115 - mean_squared_error: 0.0632 - acc: 0.0625\n",
      "Epoch 75/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4113 - mean_squared_error: 0.0632 - acc: 0.0625\n",
      "Epoch 76/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4112 - mean_squared_error: 0.0632 - acc: 0.0625\n",
      "Epoch 77/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4111 - mean_squared_error: 0.0632 - acc: 0.0625\n",
      "Epoch 78/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4110 - mean_squared_error: 0.0632 - acc: 0.0625\n",
      "Epoch 79/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4109 - mean_squared_error: 0.0632 - acc: 0.0625\n",
      "Epoch 80/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4107 - mean_squared_error: 0.0632 - acc: 0.0000e+00\n",
      "Epoch 81/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4106 - mean_squared_error: 0.0632 - acc: 0.0625\n",
      "Epoch 82/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4105 - mean_squared_error: 0.0632 - acc: 0.0625\n",
      "Epoch 83/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4104 - mean_squared_error: 0.0632 - acc: 0.0625\n",
      "Epoch 84/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4102 - mean_squared_error: 0.0632 - acc: 0.1250\n",
      "Epoch 85/1000\n",
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      "Epoch 189/1000\n",
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      "Epoch 190/1000\n",
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      "Epoch 191/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4037 - mean_squared_error: 0.0630 - acc: 0.0625\n",
      "Epoch 192/1000\n",
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      "Epoch 198/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4034 - mean_squared_error: 0.0630 - acc: 0.1250\n",
      "Epoch 199/1000\n",
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      "Epoch 202/1000\n",
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      "Epoch 225/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4026 - mean_squared_error: 0.0629 - acc: 0.0625\n",
      "Epoch 226/1000\n",
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      "Epoch 227/1000\n",
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      "Epoch 228/1000\n",
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      "Epoch 229/1000\n",
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      "Epoch 230/1000\n",
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      "Epoch 232/1000\n",
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      "Epoch 233/1000\n",
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      "Epoch 234/1000\n",
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      "Epoch 235/1000\n",
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      "Epoch 236/1000\n",
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      "Epoch 237/1000\n",
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      "Epoch 238/1000\n",
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      "Epoch 239/1000\n",
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      "Epoch 243/1000\n",
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      "Epoch 245/1000\n",
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      "Epoch 246/1000\n",
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      "Epoch 247/1000\n",
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      "Epoch 248/1000\n",
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      "Epoch 249/1000\n",
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      "Epoch 250/1000\n",
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      "Epoch 251/1000\n",
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      "Epoch 252/1000\n",
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      "Epoch 253/1000\n",
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      "Epoch 254/1000\n",
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      "Epoch 255/1000\n",
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      "Epoch 256/1000\n",
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      "Epoch 257/1000\n",
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      "Epoch 258/1000\n",
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      "Epoch 259/1000\n",
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      "Epoch 260/1000\n",
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      "Epoch 261/1000\n",
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      "Epoch 262/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4016 - mean_squared_error: 0.0629 - acc: 0.0000e+00\n",
      "Epoch 263/1000\n",
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      "Epoch 264/1000\n",
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      "Epoch 265/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4016 - mean_squared_error: 0.0629 - acc: 0.0000e+00\n",
      "Epoch 266/1000\n",
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      "Epoch 267/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4015 - mean_squared_error: 0.0629 - acc: 0.0000e+00\n",
      "Epoch 268/1000\n",
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      "Epoch 269/1000\n",
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      "Epoch 270/1000\n",
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      "Epoch 271/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4014 - mean_squared_error: 0.0629 - acc: 0.0625\n",
      "Epoch 272/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4014 - mean_squared_error: 0.0629 - acc: 0.1875\n",
      "Epoch 273/1000\n",
      "16/16 [==============================] - 0s 3ms/sample - loss: 1.4014 - mean_squared_error: 0.0629 - acc: 0.0000e+00\n",
      "Epoch 274/1000\n",
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      "Epoch 275/1000\n",
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      "Epoch 276/1000\n",
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      "Epoch 277/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4013 - mean_squared_error: 0.0629 - acc: 0.1250\n",
      "Epoch 278/1000\n",
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      "Epoch 279/1000\n",
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      "Epoch 280/1000\n",
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      "Epoch 281/1000\n",
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      "Epoch 282/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4012 - mean_squared_error: 0.0629 - acc: 0.0000e+00\n",
      "Epoch 283/1000\n",
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      "Epoch 284/1000\n",
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      "Epoch 285/1000\n",
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      "Epoch 286/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4011 - mean_squared_error: 0.0629 - acc: 0.0000e+00\n",
      "Epoch 287/1000\n",
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      "Epoch 288/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4011 - mean_squared_error: 0.0629 - acc: 0.0625\n",
      "Epoch 289/1000\n",
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      "Epoch 290/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4010 - mean_squared_error: 0.0629 - acc: 0.0000e+00\n",
      "Epoch 291/1000\n",
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      "Epoch 292/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4010 - mean_squared_error: 0.0629 - acc: 0.1250\n",
      "Epoch 293/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4010 - mean_squared_error: 0.0629 - acc: 0.0000e+00\n",
      "Epoch 294/1000\n",
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      "Epoch 295/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4009 - mean_squared_error: 0.0629 - acc: 0.0625\n",
      "Epoch 296/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4009 - mean_squared_error: 0.0629 - acc: 0.1250\n",
      "Epoch 297/1000\n",
      "16/16 [==============================] - 0s 997us/sample - loss: 1.4009 - mean_squared_error: 0.0629 - acc: 0.0625\n",
      "Epoch 298/1000\n",
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      "Epoch 299/1000\n",
      "16/16 [==============================] - 0s 997us/sample - loss: 1.4009 - mean_squared_error: 0.0629 - acc: 0.0625\n",
      "Epoch 300/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4008 - mean_squared_error: 0.0629 - acc: 0.1250\n",
      "Epoch 301/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4008 - mean_squared_error: 0.0629 - acc: 0.1250\n",
      "Epoch 302/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4008 - mean_squared_error: 0.0629 - acc: 0.0000e+00\n",
      "Epoch 303/1000\n",
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      "Epoch 304/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4008 - mean_squared_error: 0.0629 - acc: 0.1250\n",
      "Epoch 305/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4007 - mean_squared_error: 0.0629 - acc: 0.0625\n",
      "Epoch 306/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4007 - mean_squared_error: 0.0629 - acc: 0.0625\n",
      "Epoch 307/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4007 - mean_squared_error: 0.0629 - acc: 0.0625\n",
      "Epoch 308/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4007 - mean_squared_error: 0.0629 - acc: 0.0625\n",
      "Epoch 309/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4007 - mean_squared_error: 0.0629 - acc: 0.0625\n",
      "Epoch 310/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4006 - mean_squared_error: 0.0629 - acc: 0.0000e+00\n",
      "Epoch 311/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4006 - mean_squared_error: 0.0629 - acc: 0.0000e+00\n",
      "Epoch 312/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4006 - mean_squared_error: 0.0629 - acc: 0.0000e+00\n",
      "Epoch 313/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4006 - mean_squared_error: 0.0629 - acc: 0.0625\n",
      "Epoch 314/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4006 - mean_squared_error: 0.0629 - acc: 0.0625\n",
      "Epoch 315/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4005 - mean_squared_error: 0.0629 - acc: 0.0625\n",
      "Epoch 316/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4005 - mean_squared_error: 0.0629 - acc: 0.1875\n",
      "Epoch 317/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4005 - mean_squared_error: 0.0629 - acc: 0.0625\n",
      "Epoch 318/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4005 - mean_squared_error: 0.0629 - acc: 0.0000e+00\n",
      "Epoch 319/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4005 - mean_squared_error: 0.0629 - acc: 0.0000e+00\n",
      "Epoch 320/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4005 - mean_squared_error: 0.0629 - acc: 0.1875\n",
      "Epoch 321/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4004 - mean_squared_error: 0.0629 - acc: 0.0625\n",
      "Epoch 322/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4004 - mean_squared_error: 0.0629 - acc: 0.0625\n",
      "Epoch 323/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4004 - mean_squared_error: 0.0629 - acc: 0.1875\n",
      "Epoch 324/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4004 - mean_squared_error: 0.0629 - acc: 0.0000e+00\n",
      "Epoch 325/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4004 - mean_squared_error: 0.0629 - acc: 0.0625\n",
      "Epoch 326/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4004 - mean_squared_error: 0.0629 - acc: 0.0625\n",
      "Epoch 327/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4003 - mean_squared_error: 0.0629 - acc: 0.0625\n",
      "Epoch 328/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4003 - mean_squared_error: 0.0629 - acc: 0.1250\n",
      "Epoch 329/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4003 - mean_squared_error: 0.0629 - acc: 0.0625\n",
      "Epoch 330/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4003 - mean_squared_error: 0.0629 - acc: 0.0625\n",
      "Epoch 331/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4003 - mean_squared_error: 0.0629 - acc: 0.0625\n",
      "Epoch 332/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4002 - mean_squared_error: 0.0629 - acc: 0.0000e+00\n",
      "Epoch 333/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4002 - mean_squared_error: 0.0629 - acc: 0.2500\n",
      "Epoch 334/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4002 - mean_squared_error: 0.0629 - acc: 0.0000e+00\n",
      "Epoch 335/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4002 - mean_squared_error: 0.0629 - acc: 0.0625\n",
      "Epoch 336/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4002 - mean_squared_error: 0.0629 - acc: 0.0625\n",
      "Epoch 337/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4002 - mean_squared_error: 0.0629 - acc: 0.1875\n",
      "Epoch 338/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4001 - mean_squared_error: 0.0629 - acc: 0.1250\n",
      "Epoch 339/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4001 - mean_squared_error: 0.0629 - acc: 0.1250\n",
      "Epoch 340/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4001 - mean_squared_error: 0.0629 - acc: 0.0000e+00\n",
      "Epoch 341/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4001 - mean_squared_error: 0.0629 - acc: 0.0000e+00\n",
      "Epoch 342/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4001 - mean_squared_error: 0.0629 - acc: 0.0625\n",
      "Epoch 343/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4001 - mean_squared_error: 0.0629 - acc: 0.1875\n",
      "Epoch 344/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4000 - mean_squared_error: 0.0629 - acc: 0.1250\n",
      "Epoch 345/1000\n",
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      "Epoch 346/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4000 - mean_squared_error: 0.0629 - acc: 0.0625\n",
      "Epoch 347/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4000 - mean_squared_error: 0.0629 - acc: 0.1875\n",
      "Epoch 348/1000\n",
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      "Epoch 349/1000\n",
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      "Epoch 350/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.4000 - mean_squared_error: 0.0629 - acc: 0.0000e+00\n",
      "Epoch 351/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3999 - mean_squared_error: 0.0629 - acc: 0.0625\n",
      "Epoch 352/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3999 - mean_squared_error: 0.0629 - acc: 0.0625\n",
      "Epoch 353/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3999 - mean_squared_error: 0.0629 - acc: 0.1250\n",
      "Epoch 354/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3999 - mean_squared_error: 0.0629 - acc: 0.0000e+00\n",
      "Epoch 355/1000\n",
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      "Epoch 356/1000\n",
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      "Epoch 357/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3999 - mean_squared_error: 0.0629 - acc: 0.0000e+00\n",
      "Epoch 358/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3998 - mean_squared_error: 0.0629 - acc: 0.0625\n",
      "Epoch 359/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3998 - mean_squared_error: 0.0629 - acc: 0.1250\n",
      "Epoch 360/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3998 - mean_squared_error: 0.0629 - acc: 0.0000e+00\n",
      "Epoch 361/1000\n",
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      "Epoch 362/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3998 - mean_squared_error: 0.0629 - acc: 0.0000e+00\n",
      "Epoch 363/1000\n",
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      "Epoch 364/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3997 - mean_squared_error: 0.0629 - acc: 0.1250\n",
      "Epoch 365/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3997 - mean_squared_error: 0.0629 - acc: 0.0625\n",
      "Epoch 366/1000\n",
      "16/16 [==============================] - 0s 2ms/sample - loss: 1.3997 - mean_squared_error: 0.0629 - acc: 0.0625\n",
      "Epoch 367/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3997 - mean_squared_error: 0.0629 - acc: 0.0625\n",
      "Epoch 368/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3997 - mean_squared_error: 0.0629 - acc: 0.0625\n",
      "Epoch 369/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3997 - mean_squared_error: 0.0628 - acc: 0.1250\n",
      "Epoch 370/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3996 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 371/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3996 - mean_squared_error: 0.0628 - acc: 0.0000e+00\n",
      "Epoch 372/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3996 - mean_squared_error: 0.0628 - acc: 0.1250\n",
      "Epoch 373/1000\n",
      "16/16 [==============================] - 0s 2ms/sample - loss: 1.3996 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 374/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3996 - mean_squared_error: 0.0628 - acc: 0.1250\n",
      "Epoch 375/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3996 - mean_squared_error: 0.0628 - acc: 0.1250\n",
      "Epoch 376/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3996 - mean_squared_error: 0.0628 - acc: 0.0000e+00\n",
      "Epoch 377/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3996 - mean_squared_error: 0.0628 - acc: 0.0000e+00\n",
      "Epoch 378/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3995 - mean_squared_error: 0.0628 - acc: 0.1250\n",
      "Epoch 379/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3995 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 380/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3995 - mean_squared_error: 0.0628 - acc: 0.1250\n",
      "Epoch 381/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3995 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 382/1000\n",
      "16/16 [==============================] - 0s 2ms/sample - loss: 1.3995 - mean_squared_error: 0.0628 - acc: 0.1250\n",
      "Epoch 383/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3995 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 384/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3995 - mean_squared_error: 0.0628 - acc: 0.1250\n",
      "Epoch 385/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3994 - mean_squared_error: 0.0628 - acc: 0.0000e+00\n",
      "Epoch 386/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3994 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 387/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3994 - mean_squared_error: 0.0628 - acc: 0.1875\n",
      "Epoch 388/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3994 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 389/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3994 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 390/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3994 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 391/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3994 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 392/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3993 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 393/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3993 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 394/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3993 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 395/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3993 - mean_squared_error: 0.0628 - acc: 0.2500\n",
      "Epoch 396/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3993 - mean_squared_error: 0.0628 - acc: 0.1250\n",
      "Epoch 397/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3993 - mean_squared_error: 0.0628 - acc: 0.1250\n",
      "Epoch 398/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3993 - mean_squared_error: 0.0628 - acc: 0.0000e+00\n",
      "Epoch 399/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3992 - mean_squared_error: 0.0628 - acc: 0.0000e+00\n",
      "Epoch 400/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3992 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 401/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3992 - mean_squared_error: 0.0628 - acc: 0.1875\n",
      "Epoch 402/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3992 - mean_squared_error: 0.0628 - acc: 0.0000e+00\n",
      "Epoch 403/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3992 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 404/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3992 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 405/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3992 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 406/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3992 - mean_squared_error: 0.0628 - acc: 0.0000e+00\n",
      "Epoch 407/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3991 - mean_squared_error: 0.0628 - acc: 0.0000e+00\n",
      "Epoch 408/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3991 - mean_squared_error: 0.0628 - acc: 0.1250\n",
      "Epoch 409/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3991 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 410/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3991 - mean_squared_error: 0.0628 - acc: 0.0000e+00\n",
      "Epoch 411/1000\n",
      "16/16 [==============================] - 0s 2ms/sample - loss: 1.3991 - mean_squared_error: 0.0628 - acc: 0.0000e+00\n",
      "Epoch 412/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3991 - mean_squared_error: 0.0628 - acc: 0.1875\n",
      "Epoch 413/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3991 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 414/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3991 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 415/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3990 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 416/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3990 - mean_squared_error: 0.0628 - acc: 0.0000e+00\n",
      "Epoch 417/1000\n",
      "16/16 [==============================] - 0s 2ms/sample - loss: 1.3990 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 418/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3990 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 419/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3990 - mean_squared_error: 0.0628 - acc: 0.0000e+00\n",
      "Epoch 420/1000\n",
      "16/16 [==============================] - 0s 2ms/sample - loss: 1.3990 - mean_squared_error: 0.0628 - acc: 0.0000e+00\n",
      "Epoch 421/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3990 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 422/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3990 - mean_squared_error: 0.0628 - acc: 0.0000e+00\n",
      "Epoch 423/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3989 - mean_squared_error: 0.0628 - acc: 0.0000e+00\n",
      "Epoch 424/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3989 - mean_squared_error: 0.0628 - acc: 0.0000e+00\n",
      "Epoch 425/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3989 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 426/1000\n",
      "16/16 [==============================] - 0s 2ms/sample - loss: 1.3989 - mean_squared_error: 0.0628 - acc: 0.0000e+00\n",
      "Epoch 427/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3989 - mean_squared_error: 0.0628 - acc: 0.0000e+00\n",
      "Epoch 428/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3989 - mean_squared_error: 0.0628 - acc: 0.1250\n",
      "Epoch 429/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3989 - mean_squared_error: 0.0628 - acc: 0.0000e+00\n",
      "Epoch 430/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3989 - mean_squared_error: 0.0628 - acc: 0.1250\n",
      "Epoch 431/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3988 - mean_squared_error: 0.0628 - acc: 0.0000e+00\n",
      "Epoch 432/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3988 - mean_squared_error: 0.0628 - acc: 0.1250\n",
      "Epoch 433/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3988 - mean_squared_error: 0.0628 - acc: 0.1250\n",
      "Epoch 434/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3988 - mean_squared_error: 0.0628 - acc: 0.0000e+00\n",
      "Epoch 435/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3988 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 436/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3988 - mean_squared_error: 0.0628 - acc: 0.1875\n",
      "Epoch 437/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3988 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 438/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3988 - mean_squared_error: 0.0628 - acc: 0.0000e+00\n",
      "Epoch 439/1000\n",
      "16/16 [==============================] - 0s 2ms/sample - loss: 1.3988 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 440/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3987 - mean_squared_error: 0.0628 - acc: 0.0000e+00\n",
      "Epoch 441/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3987 - mean_squared_error: 0.0628 - acc: 0.0000e+00\n",
      "Epoch 442/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3987 - mean_squared_error: 0.0628 - acc: 0.0000e+00\n",
      "Epoch 443/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3987 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 444/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3987 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 445/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3987 - mean_squared_error: 0.0628 - acc: 0.1250\n",
      "Epoch 446/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3987 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 447/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3987 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 448/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3986 - mean_squared_error: 0.0628 - acc: 0.0000e+00\n",
      "Epoch 449/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3986 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 450/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3986 - mean_squared_error: 0.0628 - acc: 0.0000e+00\n",
      "Epoch 451/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3986 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 452/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3986 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 453/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3986 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 454/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3986 - mean_squared_error: 0.0628 - acc: 0.0000e+00\n",
      "Epoch 455/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3986 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 456/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3986 - mean_squared_error: 0.0628 - acc: 0.1250\n",
      "Epoch 457/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3986 - mean_squared_error: 0.0628 - acc: 0.0000e+00\n",
      "Epoch 458/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3985 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 459/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3985 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 460/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3985 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 461/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3985 - mean_squared_error: 0.0628 - acc: 0.0000e+00\n",
      "Epoch 462/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3985 - mean_squared_error: 0.0628 - acc: 0.1250\n",
      "Epoch 463/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3985 - mean_squared_error: 0.0628 - acc: 0.1875\n",
      "Epoch 464/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3985 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 465/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3985 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 466/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3985 - mean_squared_error: 0.0628 - acc: 0.0000e+00\n",
      "Epoch 467/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3984 - mean_squared_error: 0.0628 - acc: 0.1875\n",
      "Epoch 468/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3984 - mean_squared_error: 0.0628 - acc: 0.1250\n",
      "Epoch 469/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3984 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 470/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3984 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 471/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3984 - mean_squared_error: 0.0628 - acc: 0.1250\n",
      "Epoch 472/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3984 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 473/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3984 - mean_squared_error: 0.0628 - acc: 0.1250\n",
      "Epoch 474/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3984 - mean_squared_error: 0.0628 - acc: 0.1875\n",
      "Epoch 475/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3984 - mean_squared_error: 0.0628 - acc: 0.1250\n",
      "Epoch 476/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3983 - mean_squared_error: 0.0628 - acc: 0.1250\n",
      "Epoch 477/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3983 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 478/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3983 - mean_squared_error: 0.0628 - acc: 0.2500\n",
      "Epoch 479/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3983 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 480/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3983 - mean_squared_error: 0.0628 - acc: 0.0000e+00\n",
      "Epoch 481/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3983 - mean_squared_error: 0.0628 - acc: 0.1875\n",
      "Epoch 482/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3983 - mean_squared_error: 0.0628 - acc: 0.1250\n",
      "Epoch 483/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3983 - mean_squared_error: 0.0628 - acc: 0.0000e+00\n",
      "Epoch 484/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3983 - mean_squared_error: 0.0628 - acc: 0.0000e+00\n",
      "Epoch 485/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3983 - mean_squared_error: 0.0628 - acc: 0.0000e+00\n",
      "Epoch 486/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3982 - mean_squared_error: 0.0628 - acc: 0.1250\n",
      "Epoch 487/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3982 - mean_squared_error: 0.0628 - acc: 0.0000e+00\n",
      "Epoch 488/1000\n",
      "16/16 [==============================] - 0s 2ms/sample - loss: 1.3982 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 489/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3982 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 490/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3982 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 491/1000\n",
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      "Epoch 492/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3982 - mean_squared_error: 0.0628 - acc: 0.1250\n",
      "Epoch 493/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3982 - mean_squared_error: 0.0628 - acc: 0.0000e+00\n",
      "Epoch 494/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3982 - mean_squared_error: 0.0628 - acc: 0.1875\n",
      "Epoch 495/1000\n",
      "16/16 [==============================] - 0s 2ms/sample - loss: 1.3981 - mean_squared_error: 0.0628 - acc: 0.2500\n",
      "Epoch 496/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3981 - mean_squared_error: 0.0628 - acc: 0.0000e+00\n",
      "Epoch 497/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3981 - mean_squared_error: 0.0628 - acc: 0.0000e+00\n",
      "Epoch 498/1000\n",
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      "Epoch 499/1000\n",
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      "Epoch 500/1000\n",
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      "Epoch 501/1000\n",
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      "Epoch 502/1000\n",
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      "Epoch 503/1000\n",
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      "Epoch 504/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3981 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 505/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3981 - mean_squared_error: 0.0628 - acc: 0.1250\n",
      "Epoch 506/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3980 - mean_squared_error: 0.0628 - acc: 0.0000e+00\n",
      "Epoch 507/1000\n",
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      "Epoch 508/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3980 - mean_squared_error: 0.0628 - acc: 0.0000e+00\n",
      "Epoch 509/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3980 - mean_squared_error: 0.0628 - acc: 0.1250\n",
      "Epoch 510/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3980 - mean_squared_error: 0.0628 - acc: 0.0000e+00\n",
      "Epoch 511/1000\n",
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      "Epoch 512/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3980 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 513/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3980 - mean_squared_error: 0.0628 - acc: 0.1250\n",
      "Epoch 514/1000\n",
      "16/16 [==============================] - 0s 2ms/sample - loss: 1.3980 - mean_squared_error: 0.0628 - acc: 0.0000e+00\n",
      "Epoch 515/1000\n",
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      "Epoch 516/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3979 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 517/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3979 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 518/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3979 - mean_squared_error: 0.0628 - acc: 0.0000e+00\n",
      "Epoch 519/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3979 - mean_squared_error: 0.0628 - acc: 0.0000e+00\n",
      "Epoch 520/1000\n",
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      "Epoch 521/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3979 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 522/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3979 - mean_squared_error: 0.0628 - acc: 0.0000e+00\n",
      "Epoch 523/1000\n",
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      "Epoch 524/1000\n",
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      "Epoch 525/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3979 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 526/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3979 - mean_squared_error: 0.0628 - acc: 0.1250\n",
      "Epoch 527/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3978 - mean_squared_error: 0.0628 - acc: 0.0000e+00\n",
      "Epoch 528/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3978 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 529/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3978 - mean_squared_error: 0.0628 - acc: 0.0000e+00\n",
      "Epoch 530/1000\n",
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      "Epoch 531/1000\n",
      "16/16 [==============================] - 0s 2ms/sample - loss: 1.3978 - mean_squared_error: 0.0628 - acc: 0.0000e+00\n",
      "Epoch 532/1000\n",
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      "Epoch 533/1000\n",
      "16/16 [==============================] - 0s 2ms/sample - loss: 1.3978 - mean_squared_error: 0.0628 - acc: 0.0000e+00\n",
      "Epoch 534/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3978 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 535/1000\n",
      "16/16 [==============================] - 0s 2ms/sample - loss: 1.3978 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 536/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3978 - mean_squared_error: 0.0628 - acc: 0.0000e+00\n",
      "Epoch 537/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3978 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 538/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3978 - mean_squared_error: 0.0628 - acc: 0.0000e+00\n",
      "Epoch 539/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3977 - mean_squared_error: 0.0628 - acc: 0.1875\n",
      "Epoch 540/1000\n",
      "16/16 [==============================] - 0s 2ms/sample - loss: 1.3977 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 541/1000\n",
      "16/16 [==============================] - 0s 2ms/sample - loss: 1.3977 - mean_squared_error: 0.0628 - acc: 0.1875\n",
      "Epoch 542/1000\n",
      "16/16 [==============================] - 0s 2ms/sample - loss: 1.3977 - mean_squared_error: 0.0628 - acc: 0.0000e+00\n",
      "Epoch 543/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3977 - mean_squared_error: 0.0628 - acc: 0.0000e+00\n",
      "Epoch 544/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3977 - mean_squared_error: 0.0628 - acc: 0.1875\n",
      "Epoch 545/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3977 - mean_squared_error: 0.0628 - acc: 0.1250\n",
      "Epoch 546/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3977 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 547/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3977 - mean_squared_error: 0.0628 - acc: 0.0000e+00\n",
      "Epoch 548/1000\n",
      "16/16 [==============================] - 0s 2ms/sample - loss: 1.3977 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 549/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3977 - mean_squared_error: 0.0628 - acc: 0.1250\n",
      "Epoch 550/1000\n",
      "16/16 [==============================] - 0s 2ms/sample - loss: 1.3976 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 551/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3976 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 552/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3976 - mean_squared_error: 0.0628 - acc: 0.0000e+00\n",
      "Epoch 553/1000\n",
      "16/16 [==============================] - 0s 2ms/sample - loss: 1.3976 - mean_squared_error: 0.0628 - acc: 0.0000e+00\n",
      "Epoch 554/1000\n",
      "16/16 [==============================] - 0s 2ms/sample - loss: 1.3976 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 555/1000\n",
      "16/16 [==============================] - 0s 2ms/sample - loss: 1.3976 - mean_squared_error: 0.0628 - acc: 0.1250\n",
      "Epoch 556/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3976 - mean_squared_error: 0.0628 - acc: 0.1250\n",
      "Epoch 557/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3976 - mean_squared_error: 0.0628 - acc: 0.1250\n",
      "Epoch 558/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3976 - mean_squared_error: 0.0628 - acc: 0.1250\n",
      "Epoch 559/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3976 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 560/1000\n",
      "16/16 [==============================] - 0s 2ms/sample - loss: 1.3976 - mean_squared_error: 0.0628 - acc: 0.0000e+00\n",
      "Epoch 561/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3976 - mean_squared_error: 0.0628 - acc: 0.1250\n",
      "Epoch 562/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3975 - mean_squared_error: 0.0628 - acc: 0.0000e+00\n",
      "Epoch 563/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3975 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 564/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3975 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 565/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3975 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 566/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3975 - mean_squared_error: 0.0628 - acc: 0.1875\n",
      "Epoch 567/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3975 - mean_squared_error: 0.0628 - acc: 0.1250\n",
      "Epoch 568/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3975 - mean_squared_error: 0.0628 - acc: 0.0000e+00\n",
      "Epoch 569/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3975 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 570/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3975 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 571/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3975 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 572/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3975 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 573/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3975 - mean_squared_error: 0.0628 - acc: 0.1250\n",
      "Epoch 574/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3974 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 575/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3974 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 576/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3974 - mean_squared_error: 0.0628 - acc: 0.0000e+00\n",
      "Epoch 577/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3974 - mean_squared_error: 0.0628 - acc: 0.0000e+00\n",
      "Epoch 578/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3974 - mean_squared_error: 0.0628 - acc: 0.0000e+00\n",
      "Epoch 579/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3974 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 580/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3974 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 581/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3974 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 582/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3974 - mean_squared_error: 0.0628 - acc: 0.0000e+00\n",
      "Epoch 583/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3974 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 584/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3974 - mean_squared_error: 0.0628 - acc: 0.1250\n",
      "Epoch 585/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3974 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 586/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3973 - mean_squared_error: 0.0628 - acc: 0.1250\n",
      "Epoch 587/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3973 - mean_squared_error: 0.0628 - acc: 0.1250\n",
      "Epoch 588/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3973 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 589/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3973 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 590/1000\n",
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      "Epoch 591/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3973 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 592/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3973 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 593/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3973 - mean_squared_error: 0.0628 - acc: 0.1250\n",
      "Epoch 594/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3973 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 595/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3973 - mean_squared_error: 0.0628 - acc: 0.1250\n",
      "Epoch 596/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3973 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 597/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3973 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 598/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3973 - mean_squared_error: 0.0628 - acc: 0.1250\n",
      "Epoch 599/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3972 - mean_squared_error: 0.0628 - acc: 0.0000e+00\n",
      "Epoch 600/1000\n",
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      "Epoch 601/1000\n",
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      "Epoch 602/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3972 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 603/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3972 - mean_squared_error: 0.0628 - acc: 0.0000e+00\n",
      "Epoch 604/1000\n",
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      "Epoch 605/1000\n",
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      "Epoch 606/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3972 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 607/1000\n",
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      "Epoch 608/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3972 - mean_squared_error: 0.0628 - acc: 0.1875\n",
      "Epoch 609/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3972 - mean_squared_error: 0.0628 - acc: 0.0000e+00\n",
      "Epoch 610/1000\n",
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      "Epoch 611/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3972 - mean_squared_error: 0.0628 - acc: 0.0000e+00\n",
      "Epoch 612/1000\n",
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      "Epoch 613/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3971 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 614/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3971 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 615/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3971 - mean_squared_error: 0.0628 - acc: 0.0000e+00\n",
      "Epoch 616/1000\n",
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      "Epoch 617/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3971 - mean_squared_error: 0.0628 - acc: 0.0000e+00\n",
      "Epoch 618/1000\n",
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      "Epoch 619/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3971 - mean_squared_error: 0.0628 - acc: 0.0000e+00\n",
      "Epoch 620/1000\n",
      "16/16 [==============================] - 0s 2ms/sample - loss: 1.3971 - mean_squared_error: 0.0628 - acc: 0.1250\n",
      "Epoch 621/1000\n",
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      "Epoch 622/1000\n",
      "16/16 [==============================] - 0s 2ms/sample - loss: 1.3971 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 623/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3971 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 624/1000\n",
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      "Epoch 625/1000\n",
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      "Epoch 626/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3970 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 627/1000\n",
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      "Epoch 628/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3970 - mean_squared_error: 0.0628 - acc: 0.1250\n",
      "Epoch 629/1000\n",
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      "Epoch 630/1000\n",
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      "Epoch 631/1000\n",
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      "Epoch 632/1000\n",
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      "Epoch 633/1000\n",
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      "Epoch 634/1000\n",
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      "Epoch 635/1000\n",
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      "Epoch 636/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3970 - mean_squared_error: 0.0628 - acc: 0.0000e+00\n",
      "Epoch 637/1000\n",
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      "Epoch 638/1000\n",
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      "Epoch 639/1000\n",
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      "Epoch 640/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3969 - mean_squared_error: 0.0628 - acc: 0.0000e+00\n",
      "Epoch 641/1000\n",
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      "Epoch 642/1000\n",
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      "Epoch 643/1000\n",
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      "Epoch 644/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3969 - mean_squared_error: 0.0628 - acc: 0.0000e+00\n",
      "Epoch 645/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3969 - mean_squared_error: 0.0628 - acc: 0.0000e+00\n",
      "Epoch 646/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3969 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 647/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3969 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 648/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3969 - mean_squared_error: 0.0628 - acc: 0.1250\n",
      "Epoch 649/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3969 - mean_squared_error: 0.0628 - acc: 0.0000e+00\n",
      "Epoch 650/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3969 - mean_squared_error: 0.0628 - acc: 0.0000e+00\n",
      "Epoch 651/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3969 - mean_squared_error: 0.0628 - acc: 0.1250\n",
      "Epoch 652/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3969 - mean_squared_error: 0.0628 - acc: 0.1250\n",
      "Epoch 653/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3969 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 654/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3969 - mean_squared_error: 0.0628 - acc: 0.1250\n",
      "Epoch 655/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3968 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 656/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3968 - mean_squared_error: 0.0628 - acc: 0.0000e+00\n",
      "Epoch 657/1000\n",
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      "Epoch 658/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3968 - mean_squared_error: 0.0628 - acc: 0.0000e+00\n",
      "Epoch 659/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3968 - mean_squared_error: 0.0628 - acc: 0.1875\n",
      "Epoch 660/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3968 - mean_squared_error: 0.0628 - acc: 0.0000e+00\n",
      "Epoch 661/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3968 - mean_squared_error: 0.0628 - acc: 0.0000e+00\n",
      "Epoch 662/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3968 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 663/1000\n",
      "16/16 [==============================] - 0s 2ms/sample - loss: 1.3968 - mean_squared_error: 0.0628 - acc: 0.1250\n",
      "Epoch 664/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3968 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 665/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3968 - mean_squared_error: 0.0628 - acc: 0.1250\n",
      "Epoch 666/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3968 - mean_squared_error: 0.0628 - acc: 0.0000e+00\n",
      "Epoch 667/1000\n",
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      "Epoch 668/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3968 - mean_squared_error: 0.0628 - acc: 0.0000e+00\n",
      "Epoch 669/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3968 - mean_squared_error: 0.0628 - acc: 0.0000e+00\n",
      "Epoch 670/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3967 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 671/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3967 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 672/1000\n",
      "16/16 [==============================] - 0s 2ms/sample - loss: 1.3967 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 673/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3967 - mean_squared_error: 0.0628 - acc: 0.0000e+00\n",
      "Epoch 674/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3967 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 675/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3967 - mean_squared_error: 0.0628 - acc: 0.1875\n",
      "Epoch 676/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3967 - mean_squared_error: 0.0628 - acc: 0.0000e+00\n",
      "Epoch 677/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3967 - mean_squared_error: 0.0628 - acc: 0.1250\n",
      "Epoch 678/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3967 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 679/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3967 - mean_squared_error: 0.0628 - acc: 0.1250\n",
      "Epoch 680/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3967 - mean_squared_error: 0.0628 - acc: 0.0000e+00\n",
      "Epoch 681/1000\n",
      "16/16 [==============================] - 0s 2ms/sample - loss: 1.3967 - mean_squared_error: 0.0628 - acc: 0.1250\n",
      "Epoch 682/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3967 - mean_squared_error: 0.0628 - acc: 0.1250\n",
      "Epoch 683/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3967 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 684/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3967 - mean_squared_error: 0.0628 - acc: 0.0000e+00\n",
      "Epoch 685/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3966 - mean_squared_error: 0.0628 - acc: 0.1875\n",
      "Epoch 686/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3966 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 687/1000\n",
      "16/16 [==============================] - 0s 2ms/sample - loss: 1.3966 - mean_squared_error: 0.0628 - acc: 0.0000e+00\n",
      "Epoch 688/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3966 - mean_squared_error: 0.0628 - acc: 0.0000e+00\n",
      "Epoch 689/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3966 - mean_squared_error: 0.0628 - acc: 0.0000e+00\n",
      "Epoch 690/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3966 - mean_squared_error: 0.0628 - acc: 0.0000e+00\n",
      "Epoch 691/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3966 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 692/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3966 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 693/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3966 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 694/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3966 - mean_squared_error: 0.0628 - acc: 0.0000e+00\n",
      "Epoch 695/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3966 - mean_squared_error: 0.0628 - acc: 0.0000e+00\n",
      "Epoch 696/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3966 - mean_squared_error: 0.0628 - acc: 0.0000e+00\n",
      "Epoch 697/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3966 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 698/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3966 - mean_squared_error: 0.0628 - acc: 0.0000e+00\n",
      "Epoch 699/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3966 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 700/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3966 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 701/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3965 - mean_squared_error: 0.0628 - acc: 0.1250\n",
      "Epoch 702/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3965 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 703/1000\n",
      "16/16 [==============================] - 0s 2ms/sample - loss: 1.3965 - mean_squared_error: 0.0628 - acc: 0.1250\n",
      "Epoch 704/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3965 - mean_squared_error: 0.0628 - acc: 0.1875\n",
      "Epoch 705/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3965 - mean_squared_error: 0.0628 - acc: 0.0000e+00\n",
      "Epoch 706/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3965 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 707/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3965 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 708/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3965 - mean_squared_error: 0.0628 - acc: 0.0000e+00\n",
      "Epoch 709/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3965 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 710/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3965 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 711/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3965 - mean_squared_error: 0.0628 - acc: 0.0000e+00\n",
      "Epoch 712/1000\n",
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      "Epoch 713/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3965 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 714/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3965 - mean_squared_error: 0.0628 - acc: 0.1250\n",
      "Epoch 715/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3965 - mean_squared_error: 0.0628 - acc: 0.1250\n",
      "Epoch 716/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3965 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 717/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3965 - mean_squared_error: 0.0628 - acc: 0.0000e+00\n",
      "Epoch 718/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3965 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 719/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3964 - mean_squared_error: 0.0628 - acc: 0.0000e+00\n",
      "Epoch 720/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3964 - mean_squared_error: 0.0628 - acc: 0.0000e+00\n",
      "Epoch 721/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3964 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 722/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3964 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 723/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3964 - mean_squared_error: 0.0628 - acc: 0.0625\n",
      "Epoch 724/1000\n",
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      "Epoch 725/1000\n",
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      "Epoch 726/1000\n",
      "16/16 [==============================] - 0s 2ms/sample - loss: 1.3964 - mean_squared_error: 0.0628 - acc: 0.0000e+00\n",
      "Epoch 727/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3964 - mean_squared_error: 0.0628 - acc: 0.1250\n",
      "Epoch 728/1000\n",
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      "Epoch 729/1000\n",
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      "Epoch 730/1000\n",
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      "Epoch 731/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3964 - mean_squared_error: 0.0628 - acc: 0.0000e+00\n",
      "Epoch 732/1000\n",
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      "Epoch 733/1000\n",
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      "Epoch 734/1000\n",
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      "Epoch 735/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3964 - mean_squared_error: 0.0628 - acc: 0.0000e+00\n",
      "Epoch 736/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3963 - mean_squared_error: 0.0628 - acc: 0.1250\n",
      "Epoch 737/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3963 - mean_squared_error: 0.0628 - acc: 0.2500\n",
      "Epoch 738/1000\n",
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      "Epoch 739/1000\n",
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      "Epoch 740/1000\n",
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      "Epoch 741/1000\n",
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      "Epoch 742/1000\n",
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      "Epoch 743/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3963 - mean_squared_error: 0.0628 - acc: 0.0000e+00\n",
      "Epoch 744/1000\n",
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      "Epoch 745/1000\n",
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      "Epoch 746/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3963 - mean_squared_error: 0.0628 - acc: 0.1250\n",
      "Epoch 747/1000\n",
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      "Epoch 748/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3963 - mean_squared_error: 0.0628 - acc: 0.0000e+00\n",
      "Epoch 749/1000\n",
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      "Epoch 750/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3963 - mean_squared_error: 0.0628 - acc: 0.1250\n",
      "Epoch 751/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3963 - mean_squared_error: 0.0628 - acc: 0.0000e+00\n",
      "Epoch 752/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3962 - mean_squared_error: 0.0628 - acc: 0.1250\n",
      "Epoch 753/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3962 - mean_squared_error: 0.0628 - acc: 0.1250\n",
      "Epoch 754/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3962 - mean_squared_error: 0.0628 - acc: 0.1875\n",
      "Epoch 755/1000\n",
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      "Epoch 756/1000\n",
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      "Epoch 757/1000\n",
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      "Epoch 758/1000\n",
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      "Epoch 759/1000\n",
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      "Epoch 760/1000\n",
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      "Epoch 761/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3962 - mean_squared_error: 0.0628 - acc: 0.0000e+00\n",
      "Epoch 762/1000\n",
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      "Epoch 763/1000\n",
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      "Epoch 764/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3962 - mean_squared_error: 0.0627 - acc: 0.0000e+00\n",
      "Epoch 765/1000\n",
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      "Epoch 766/1000\n",
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      "Epoch 767/1000\n",
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      "Epoch 768/1000\n",
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      "Epoch 769/1000\n",
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      "Epoch 770/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3962 - mean_squared_error: 0.0627 - acc: 0.0000e+00\n",
      "Epoch 771/1000\n",
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      "Epoch 772/1000\n",
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      "Epoch 773/1000\n",
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      "Epoch 774/1000\n",
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      "Epoch 775/1000\n",
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      "Epoch 776/1000\n",
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      "Epoch 777/1000\n",
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      "Epoch 778/1000\n",
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      "Epoch 779/1000\n",
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      "Epoch 780/1000\n",
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      "Epoch 781/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3961 - mean_squared_error: 0.0627 - acc: 0.0625\n",
      "Epoch 782/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3961 - mean_squared_error: 0.0627 - acc: 0.0000e+00\n",
      "Epoch 783/1000\n",
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      "Epoch 784/1000\n",
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      "Epoch 785/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3961 - mean_squared_error: 0.0627 - acc: 0.0625\n",
      "Epoch 786/1000\n",
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      "Epoch 787/1000\n",
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      "Epoch 788/1000\n",
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      "Epoch 789/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3960 - mean_squared_error: 0.0627 - acc: 0.0000e+00\n",
      "Epoch 790/1000\n",
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      "Epoch 791/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3960 - mean_squared_error: 0.0627 - acc: 0.0625\n",
      "Epoch 792/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3960 - mean_squared_error: 0.0627 - acc: 0.0625\n",
      "Epoch 793/1000\n",
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      "Epoch 794/1000\n",
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      "Epoch 795/1000\n",
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      "Epoch 796/1000\n",
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      "Epoch 797/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3960 - mean_squared_error: 0.0627 - acc: 0.0000e+00\n",
      "Epoch 798/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3960 - mean_squared_error: 0.0627 - acc: 0.1250\n",
      "Epoch 799/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3960 - mean_squared_error: 0.0627 - acc: 0.0625\n",
      "Epoch 800/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3960 - mean_squared_error: 0.0627 - acc: 0.0625\n",
      "Epoch 801/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3960 - mean_squared_error: 0.0627 - acc: 0.1875\n",
      "Epoch 802/1000\n",
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      "Epoch 803/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3960 - mean_squared_error: 0.0627 - acc: 0.1875\n",
      "Epoch 804/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3960 - mean_squared_error: 0.0627 - acc: 0.0625\n",
      "Epoch 805/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3960 - mean_squared_error: 0.0627 - acc: 0.0625\n",
      "Epoch 806/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3960 - mean_squared_error: 0.0627 - acc: 0.1250\n",
      "Epoch 807/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3960 - mean_squared_error: 0.0627 - acc: 0.0000e+00\n",
      "Epoch 808/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3960 - mean_squared_error: 0.0627 - acc: 0.0000e+00\n",
      "Epoch 809/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3959 - mean_squared_error: 0.0627 - acc: 0.0625\n",
      "Epoch 810/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3959 - mean_squared_error: 0.0627 - acc: 0.0000e+00\n",
      "Epoch 811/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3959 - mean_squared_error: 0.0627 - acc: 0.0625\n",
      "Epoch 812/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3959 - mean_squared_error: 0.0627 - acc: 0.0000e+00\n",
      "Epoch 813/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3959 - mean_squared_error: 0.0627 - acc: 0.0000e+00\n",
      "Epoch 814/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3959 - mean_squared_error: 0.0627 - acc: 0.1250\n",
      "Epoch 815/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3959 - mean_squared_error: 0.0627 - acc: 0.2500\n",
      "Epoch 816/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3959 - mean_squared_error: 0.0627 - acc: 0.0625\n",
      "Epoch 817/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3959 - mean_squared_error: 0.0627 - acc: 0.0000e+00\n",
      "Epoch 818/1000\n",
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      "Epoch 819/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3959 - mean_squared_error: 0.0627 - acc: 0.0000e+00\n",
      "Epoch 820/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3959 - mean_squared_error: 0.0627 - acc: 0.1250\n",
      "Epoch 821/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3959 - mean_squared_error: 0.0627 - acc: 0.0000e+00\n",
      "Epoch 822/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3959 - mean_squared_error: 0.0627 - acc: 0.0625\n",
      "Epoch 823/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3959 - mean_squared_error: 0.0627 - acc: 0.0000e+00\n",
      "Epoch 824/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3959 - mean_squared_error: 0.0627 - acc: 0.0000e+00\n",
      "Epoch 825/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3959 - mean_squared_error: 0.0627 - acc: 0.1250\n",
      "Epoch 826/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3959 - mean_squared_error: 0.0627 - acc: 0.1250\n",
      "Epoch 827/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3959 - mean_squared_error: 0.0627 - acc: 0.0625\n",
      "Epoch 828/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3958 - mean_squared_error: 0.0627 - acc: 0.1250\n",
      "Epoch 829/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3958 - mean_squared_error: 0.0627 - acc: 0.0625\n",
      "Epoch 830/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3958 - mean_squared_error: 0.0627 - acc: 0.0625\n",
      "Epoch 831/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3958 - mean_squared_error: 0.0627 - acc: 0.1250\n",
      "Epoch 832/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3958 - mean_squared_error: 0.0627 - acc: 0.0625\n",
      "Epoch 833/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3958 - mean_squared_error: 0.0627 - acc: 0.0000e+00\n",
      "Epoch 834/1000\n",
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      "Epoch 835/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3958 - mean_squared_error: 0.0627 - acc: 0.0000e+00\n",
      "Epoch 836/1000\n",
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      "Epoch 837/1000\n",
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      "Epoch 838/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3958 - mean_squared_error: 0.0627 - acc: 0.0000e+00\n",
      "Epoch 839/1000\n",
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      "Epoch 840/1000\n",
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      "Epoch 841/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3958 - mean_squared_error: 0.0627 - acc: 0.0000e+00\n",
      "Epoch 842/1000\n",
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      "Epoch 843/1000\n",
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      "Epoch 844/1000\n",
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      "Epoch 845/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3958 - mean_squared_error: 0.0627 - acc: 0.1250\n",
      "Epoch 846/1000\n",
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      "Epoch 847/1000\n",
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      "Epoch 848/1000\n",
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      "Epoch 849/1000\n",
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      "Epoch 850/1000\n",
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      "Epoch 851/1000\n",
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      "Epoch 852/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3957 - mean_squared_error: 0.0627 - acc: 0.0000e+00\n",
      "Epoch 853/1000\n",
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      "Epoch 854/1000\n",
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      "Epoch 855/1000\n",
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      "Epoch 856/1000\n",
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      "Epoch 857/1000\n",
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      "Epoch 858/1000\n",
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      "Epoch 859/1000\n",
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      "Epoch 860/1000\n",
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      "Epoch 861/1000\n",
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      "Epoch 862/1000\n",
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      "Epoch 863/1000\n",
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      "Epoch 864/1000\n",
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      "Epoch 865/1000\n",
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      "Epoch 866/1000\n",
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      "Epoch 867/1000\n",
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      "Epoch 868/1000\n",
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      "Epoch 869/1000\n",
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      "Epoch 870/1000\n",
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      "Epoch 871/1000\n",
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      "Epoch 872/1000\n",
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      "Epoch 873/1000\n",
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      "Epoch 874/1000\n",
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      "Epoch 875/1000\n",
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      "Epoch 876/1000\n",
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      "Epoch 877/1000\n",
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      "Epoch 878/1000\n",
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      "Epoch 879/1000\n",
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      "Epoch 880/1000\n",
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      "Epoch 881/1000\n",
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      "Epoch 882/1000\n",
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      "Epoch 883/1000\n",
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      "Epoch 884/1000\n",
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      "Epoch 885/1000\n",
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      "Epoch 886/1000\n",
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      "Epoch 887/1000\n",
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      "Epoch 888/1000\n",
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      "Epoch 889/1000\n",
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      "Epoch 890/1000\n",
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      "Epoch 891/1000\n",
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      "Epoch 892/1000\n",
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      "Epoch 893/1000\n",
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      "Epoch 894/1000\n",
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      "Epoch 895/1000\n",
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      "Epoch 896/1000\n",
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      "Epoch 897/1000\n",
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      "Epoch 898/1000\n",
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      "Epoch 899/1000\n",
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      "Epoch 900/1000\n",
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      "Epoch 901/1000\n",
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      "Epoch 902/1000\n",
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      "Epoch 903/1000\n",
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      "Epoch 904/1000\n",
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      "Epoch 905/1000\n",
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      "Epoch 906/1000\n",
      "16/16 [==============================] - ETA: 0s - loss: 1.3867 - mean_squared_error: 0.0624 - acc: 1.00 - 0s 1ms/sample - loss: 1.3955 - mean_squared_error: 0.0627 - acc: 0.0625\n",
      "Epoch 907/1000\n",
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      "Epoch 908/1000\n",
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      "Epoch 909/1000\n",
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      "Epoch 911/1000\n",
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      "Epoch 912/1000\n",
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      "Epoch 913/1000\n",
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      "Epoch 914/1000\n",
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      "Epoch 915/1000\n",
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      "Epoch 916/1000\n",
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      "Epoch 917/1000\n",
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      "Epoch 918/1000\n",
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      "Epoch 919/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3954 - mean_squared_error: 0.0627 - acc: 0.0000e+00\n",
      "Epoch 920/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3954 - mean_squared_error: 0.0627 - acc: 0.0625\n",
      "Epoch 921/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3954 - mean_squared_error: 0.0627 - acc: 0.0000e+00\n",
      "Epoch 922/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3954 - mean_squared_error: 0.0627 - acc: 0.0625\n",
      "Epoch 923/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3954 - mean_squared_error: 0.0627 - acc: 0.0000e+00\n",
      "Epoch 924/1000\n",
      "16/16 [==============================] - 0s 997us/sample - loss: 1.3954 - mean_squared_error: 0.0627 - acc: 0.0000e+00\n",
      "Epoch 925/1000\n",
      "16/16 [==============================] - ETA: 0s - loss: 1.3884 - mean_squared_error: 0.0625 - acc: 0.0000e+ - 0s 1ms/sample - loss: 1.3954 - mean_squared_error: 0.0627 - acc: 0.0000e+00\n",
      "Epoch 926/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3954 - mean_squared_error: 0.0627 - acc: 0.0625\n",
      "Epoch 927/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3954 - mean_squared_error: 0.0627 - acc: 0.0000e+00\n",
      "Epoch 928/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3954 - mean_squared_error: 0.0627 - acc: 0.0000e+00\n",
      "Epoch 929/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3954 - mean_squared_error: 0.0627 - acc: 0.0000e+00\n",
      "Epoch 930/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3954 - mean_squared_error: 0.0627 - acc: 0.0000e+00\n",
      "Epoch 931/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3954 - mean_squared_error: 0.0627 - acc: 0.0625\n",
      "Epoch 932/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3954 - mean_squared_error: 0.0627 - acc: 0.0000e+00\n",
      "Epoch 933/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3954 - mean_squared_error: 0.0627 - acc: 0.0000e+00\n",
      "Epoch 934/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3954 - mean_squared_error: 0.0627 - acc: 0.1250\n",
      "Epoch 935/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3954 - mean_squared_error: 0.0627 - acc: 0.0000e+00\n",
      "Epoch 936/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3954 - mean_squared_error: 0.0627 - acc: 0.0625\n",
      "Epoch 937/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3954 - mean_squared_error: 0.0627 - acc: 0.1250\n",
      "Epoch 938/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3953 - mean_squared_error: 0.0627 - acc: 0.1250\n",
      "Epoch 939/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3953 - mean_squared_error: 0.0627 - acc: 0.0625\n",
      "Epoch 940/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3953 - mean_squared_error: 0.0627 - acc: 0.0625\n",
      "Epoch 941/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3953 - mean_squared_error: 0.0627 - acc: 0.0000e+00\n",
      "Epoch 942/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3953 - mean_squared_error: 0.0627 - acc: 0.0000e+00\n",
      "Epoch 943/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3953 - mean_squared_error: 0.0627 - acc: 0.0625\n",
      "Epoch 944/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3953 - mean_squared_error: 0.0627 - acc: 0.0625\n",
      "Epoch 945/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3953 - mean_squared_error: 0.0627 - acc: 0.0625\n",
      "Epoch 946/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3953 - mean_squared_error: 0.0627 - acc: 0.0000e+00\n",
      "Epoch 947/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3953 - mean_squared_error: 0.0627 - acc: 0.0000e+00\n",
      "Epoch 948/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3953 - mean_squared_error: 0.0627 - acc: 0.0000e+00\n",
      "Epoch 949/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3953 - mean_squared_error: 0.0627 - acc: 0.0000e+00\n",
      "Epoch 950/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3953 - mean_squared_error: 0.0627 - acc: 0.1250\n",
      "Epoch 951/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3953 - mean_squared_error: 0.0627 - acc: 0.0000e+00\n",
      "Epoch 952/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3953 - mean_squared_error: 0.0627 - acc: 0.1875\n",
      "Epoch 953/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3953 - mean_squared_error: 0.0627 - acc: 0.0000e+00\n",
      "Epoch 954/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3953 - mean_squared_error: 0.0627 - acc: 0.0625\n",
      "Epoch 955/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3953 - mean_squared_error: 0.0627 - acc: 0.0625\n",
      "Epoch 956/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3953 - mean_squared_error: 0.0627 - acc: 0.0625\n",
      "Epoch 957/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3953 - mean_squared_error: 0.0627 - acc: 0.0000e+00\n",
      "Epoch 958/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3953 - mean_squared_error: 0.0627 - acc: 0.0000e+00\n",
      "Epoch 959/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3953 - mean_squared_error: 0.0627 - acc: 0.0000e+00\n",
      "Epoch 960/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3953 - mean_squared_error: 0.0627 - acc: 0.0000e+00\n",
      "Epoch 961/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3953 - mean_squared_error: 0.0627 - acc: 0.1250\n",
      "Epoch 962/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3953 - mean_squared_error: 0.0627 - acc: 0.0625\n",
      "Epoch 963/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3952 - mean_squared_error: 0.0627 - acc: 0.1250\n",
      "Epoch 964/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3952 - mean_squared_error: 0.0627 - acc: 0.1250\n",
      "Epoch 965/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3952 - mean_squared_error: 0.0627 - acc: 0.0625\n",
      "Epoch 966/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3952 - mean_squared_error: 0.0627 - acc: 0.0000e+00\n",
      "Epoch 967/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3952 - mean_squared_error: 0.0627 - acc: 0.0625\n",
      "Epoch 968/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3952 - mean_squared_error: 0.0627 - acc: 0.0625\n",
      "Epoch 969/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3952 - mean_squared_error: 0.0627 - acc: 0.0000e+00\n",
      "Epoch 970/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3952 - mean_squared_error: 0.0627 - acc: 0.0625\n",
      "Epoch 971/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3952 - mean_squared_error: 0.0627 - acc: 0.1250\n",
      "Epoch 972/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3952 - mean_squared_error: 0.0627 - acc: 0.0625\n",
      "Epoch 973/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3952 - mean_squared_error: 0.0627 - acc: 0.0000e+00\n",
      "Epoch 974/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3952 - mean_squared_error: 0.0627 - acc: 0.0625\n",
      "Epoch 975/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3952 - mean_squared_error: 0.0627 - acc: 0.0625\n",
      "Epoch 976/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3952 - mean_squared_error: 0.0627 - acc: 0.1875\n",
      "Epoch 977/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3952 - mean_squared_error: 0.0627 - acc: 0.0000e+00\n",
      "Epoch 978/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3952 - mean_squared_error: 0.0627 - acc: 0.0000e+00\n",
      "Epoch 979/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3952 - mean_squared_error: 0.0627 - acc: 0.0625\n",
      "Epoch 980/1000\n",
      "16/16 [==============================] - 0s 2ms/sample - loss: 1.3952 - mean_squared_error: 0.0627 - acc: 0.0000e+00\n",
      "Epoch 981/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3952 - mean_squared_error: 0.0627 - acc: 0.0625\n",
      "Epoch 982/1000\n",
      "16/16 [==============================] - 0s 997us/sample - loss: 1.3952 - mean_squared_error: 0.0627 - acc: 0.0625\n",
      "Epoch 983/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3952 - mean_squared_error: 0.0627 - acc: 0.0625\n",
      "Epoch 984/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3952 - mean_squared_error: 0.0627 - acc: 0.0000e+00\n",
      "Epoch 985/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3952 - mean_squared_error: 0.0627 - acc: 0.1250\n",
      "Epoch 986/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3952 - mean_squared_error: 0.0627 - acc: 0.0625\n",
      "Epoch 987/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3952 - mean_squared_error: 0.0627 - acc: 0.0625\n",
      "Epoch 988/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3952 - mean_squared_error: 0.0627 - acc: 0.1250\n",
      "Epoch 989/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3951 - mean_squared_error: 0.0627 - acc: 0.1875\n",
      "Epoch 990/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3951 - mean_squared_error: 0.0627 - acc: 0.0000e+00\n",
      "Epoch 991/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3951 - mean_squared_error: 0.0627 - acc: 0.0625\n",
      "Epoch 992/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3951 - mean_squared_error: 0.0627 - acc: 0.0625\n",
      "Epoch 993/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3951 - mean_squared_error: 0.0627 - acc: 0.0625\n",
      "Epoch 994/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3951 - mean_squared_error: 0.0627 - acc: 0.0000e+00\n",
      "Epoch 995/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3951 - mean_squared_error: 0.0627 - acc: 0.0000e+00\n",
      "Epoch 996/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3951 - mean_squared_error: 0.0627 - acc: 0.0625\n",
      "Epoch 997/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3951 - mean_squared_error: 0.0627 - acc: 0.0625\n",
      "Epoch 998/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3951 - mean_squared_error: 0.0627 - acc: 0.0625\n",
      "Epoch 999/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3951 - mean_squared_error: 0.0627 - acc: 0.0625\n",
      "Epoch 1000/1000\n",
      "16/16 [==============================] - 0s 1ms/sample - loss: 1.3951 - mean_squared_error: 0.0627 - acc: 0.1250\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x1bcd4ceddd8>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# train model\n",
    "model.compile(optimizer=keras.optimizers.Adagrad(0.07), loss='categorical_crossentropy', metrics=['mse', 'accuracy'])\n",
    "model.fit(train_data_encoded, train_label_encoded, batch_size=1, epochs=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "#validat\n",
    "weights = model.get_weights()\n",
    "embeddings = np.array(weights[0])\n",
    "embeddings_right = np.array(weights[1]).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12, 5)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embeddings.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12, 5)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embeddings_right.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "word_vec = dict((word, vec) for word, vec in zip(vocabulary, embeddings))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1.563953  ,  0.98907673,  0.85600865, -1.9644891 ,  0.6546681 ],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "word_vec['king']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca = PCA(n_components=2)\n",
    "x_pca = pca.fit_transform(embeddings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "for i in range(len(x_pca)):\n",
    "    term = x_pca[i]\n",
    "    ax.scatter(term[0], term[1])\n",
    "    ax.annotate(vocabulary[i], term)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#right vector\n",
    "fig, ax = plt.subplots()\n",
    "pca = PCA(n_components=2)\n",
    "x_pca = pca.fit_transform(embeddings_right)\n",
    "for i in range(len(x_pca)):\n",
    "    term = x_pca[i]\n",
    "    ax.scatter(term[0], term[1])\n",
    "    ax.annotate(vocabulary[i], term)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}