{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "11c76523",
   "metadata": {},
   "source": [
    "# creating basic model\n",
    "- with character level tokenization\n",
    "\n",
    "steps:-\n",
    "- [X] tokenization\n",
    "- [X] make a dataset pipeline\n",
    "- [X] create a model\n",
    "- [X] define/choose some metrics for evaluation\n",
    "- [X] train the model\n",
    "- [X] evalulate the model \n",
    "\n",
    "\n",
    "note:-\n",
    "- we don't accept symbols other than \" \" in tokenization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import math"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f68002ca",
   "metadata": {},
   "source": [
    "## loading the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "510a19af",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "len of training data: 755\n",
      "len of validation data: 144\n"
     ]
    }
   ],
   "source": [
    "train_file_path = \"datasets/color_name_to_colors.csv\"\n",
    "val_file_path = \"datasets/wikipedia_x11_colors.csv\"\n",
    "\n",
    "train_data=pd.read_csv(train_file_path) # loading the training data\n",
    "val_data=pd.read_csv(val_file_path) # loading the validation data\n",
    "\n",
    "print(\"len of training data:\", len(train_data))\n",
    "print(\"len of validation data:\", len(val_data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "a2dd462d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name    0\n",
      "R       0\n",
      "G       0\n",
      "B       0\n",
      "dtype: int64\n",
      "Name    0\n",
      "R       0\n",
      "G       0\n",
      "B       0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# checking if data is null\n",
    "print(train_data.isnull().sum())\n",
    "print(val_data.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "859cffe7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# preprocessing for colors without any name\n",
    "train_data['Name']=train_data['Name'].map(lambda x:x.strip(\" \").lower())\n",
    "val_data['Name']=val_data['Name'].map(lambda x:x.strip(\" \").lower())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "cc60489e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# remove empty name examples\n",
    "train_data = train_data[(train_data['Name']!='')]\n",
    "val_data = val_data[(val_data['Name']!='')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "d4e53851",
   "metadata": {},
   "outputs": [],
   "source": [
    "# clip color values\n",
    "train_data[['R','G','B']] = train_data[['R','G','B']].clip(0,255)\n",
    "val_data[['R','G','B']] = val_data[['R','G','B']].clip(0,255)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "fcda01aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# dataframe to numpy array conversion\n",
    "\n",
    "train_data = train_data.to_numpy()\n",
    "val_data = val_data.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "4407e3e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# tokenization dicts\n",
    "\n",
    "TOKENS = {chr(i) for i in range(97,122+1)}  # add lower case characters tokens\n",
    "TOKENS.add(\" \")     # add space token\n",
    "for i in range(10): TOKENS.add(str(i))    # add digits tokens\n",
    "\n",
    "token_to_idx = {token:idx for idx,token in enumerate(TOKENS)}\n",
    "idx_to_token = {idx:token for idx,token in enumerate(TOKENS)}\n",
    "# token_to_idx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "041c9fed",
   "metadata": {},
   "outputs": [],
   "source": [
    "def tokenize(name):\n",
    "    \"\"\" tokenize single name \"\"\"\n",
    "    return [token_to_idx[char] for char in name]\n",
    "\n",
    "def one_hot_encode(tokens,num_classes):\n",
    "    return tf.keras.utils.to_categorical(tokens,num_classes=num_classes)\n",
    "\n",
    "def add_padding(one_hot_vectors,num_classes,max_num_tokens):\n",
    "    ''' one_hot_vectors:np.array shape:(tokens,len(all_tokens)) '''\n",
    "    num_of_padding = max_num_tokens-len(one_hot_vectors)\n",
    "    padding = []\n",
    "    \n",
    "    for _ in range(num_of_padding):\n",
    "        padding.append(np.zeros([num_classes]))\n",
    "    padding = np.array(padding)\n",
    "\n",
    "    # print(max_num_tokens)\n",
    "    # print(num_of_padding)\n",
    "    # print(padding.shape)\n",
    "    # print(one_hot_vectors.shape)\n",
    "\n",
    "    return np.r_[padding,one_hot_vectors] if len(padding)>0 else one_hot_vectors\n",
    "\n",
    "def preprocess(names):\n",
    "    \"\"\" names: [name,name,name,...] \"\"\"\n",
    "    \n",
    "    max_num_tokens=0\n",
    "    one_hots_list = []\n",
    "\n",
    "    for name in names:\n",
    "        name = name.lower()\n",
    "        tokens = tokenize(name)\n",
    "        one_hot_vectors = one_hot_encode(tokens,len(TOKENS))\n",
    "        if len(tokens)>max_num_tokens:   max_num_tokens=len(tokens)\n",
    "        one_hots_list.append(one_hot_vectors)\n",
    "    \n",
    "    for i in range(len(one_hots_list)):\n",
    "        # we need to add padding so that all the examples have same number of tokens\n",
    "        one_hots = one_hots_list[i]\n",
    "        one_hots_list[i] = add_padding(one_hots,len(TOKENS),max_num_tokens)\n",
    "\n",
    "    return np.array(one_hots_list)\n",
    "\n",
    "\n",
    "train_name_tokens = preprocess(train_data[:,0])\n",
    "val_name_tokens = preprocess(val_data[:,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "038f9a81",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_colors = train_data[:,1:].astype(\"uint8\")\n",
    "val_colors = val_data[:,1:].astype(\"uint8\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "2f71b744",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<_ParallelBatchDataset element_spec=(TensorSpec(shape=(None, 29, 37), dtype=tf.float64, name=None), TensorSpec(shape=(None, 3), dtype=tf.float32, name=None))>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def normalize(tokens,color):\n",
    "    color=tf.cast(color,dtype=\"float32\")/255.0\n",
    "    return tokens,color\n",
    "def get_tf_dataset(names_tokens,colors,bs=32,shuffle=False):\n",
    "    ds = tf.data.Dataset.from_tensor_slices((names_tokens,colors))\n",
    "    if shuffle: ds=ds.shuffle(len(colors))\n",
    "    ds = ds.map(normalize,num_parallel_calls=tf.data.AUTOTUNE)\n",
    "    ds = ds.batch(bs,num_parallel_calls=tf.data.AUTOTUNE)\n",
    "    return ds\n",
    "\n",
    "train_ds = get_tf_dataset(train_name_tokens,train_colors,bs=32)\n",
    "train_ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "10c71e60",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(32, 29, 37)\n",
      "(32, 3)\n"
     ]
    }
   ],
   "source": [
    "for data in train_ds.take(1):\n",
    "    print(data[0].shape)\n",
    "    print(data[1].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "1501a0fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
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l6tSpNdP/8Ic/VJJU7rnnnoU+t1KpVI488shKVVVV5YknnqiZNmXKlErbtm3n+zefs78cO3ZsrWWcddZZlebNm1defPHFWtN//vOfVxo2bFh57bXXKpVKpfLCCy9UklSuuuqqWvMNGDCg0rlz58rs2bO/cKwAJSzo8/eECRPm2y/97Gc/qyy//PI1j4877rhK7969K9XV1TXbsSlTplSqqqoql112WaVSqVRmz55dWX311Sv9+vWrtU376KOPKl26dKlst912NdNatWpV+dnPfrbQcX7yySeV6urqyrrrrlv5+OOPa6b/8Y9/rCSpnHbaaTXTDjjggEqSyjnnnFMz7d13360su+yylaqqqsrvfve7munPP/98JUnl9NNPr5lW1+04AIuurvufeV1wwQXzzTPvsdgcBxxwQKVTp041j+ccK7Rs2bIyefLkWvNus802lfXWW68yY8aMmmmzZ8+ubLnllpXVV1+9Hq8MgErl27Wdn7PcDh06VN57772a6SeffHIlSWWDDTaofPrppzXTf/SjH1UaN25ca13zvoYf/OAHlXXWWecL13vhhRfO910UwHdBXc97Vyr1P/d9xx131EybNm1aZYUVVqhstNFGNdOGDx9eSVJ57rnnaqZ98sknlfbt21cOOOCALxz3nPPhK620UuX999+vmX777bdXktR8X1apzL8Pm/Pcuc8bVSr//33Q3OeEHLuwKNzKjaLmLT+PPPLIJMmf//znJKlVYU6bNi3vvPNO+vTpk//85z+ZNm1ared279691i+dO3TokDXXXDP/+c9/aqY1adIkDRp8/n/jWbNmZcqUKWnRokXWXHPN+W65kHx+66G576M8Z/lzL3PuMX744Yd55513suWWW6ZSqeSJJ56o4zsBsOSNHTs2K620UgYMGFAzrWnTpjn00EMXOH+LFi1qXSGncePG2WyzzWptI+tj1qxZuf/++/PDH/6w1hXsunXrlv79+y/wOX369En37t1rHlcqldxxxx3ZZZddUqlU8s4779T8169fv0ybNq1mez9q1KisvfbaWWuttWrNt/XWWyfJfFeK2nbbbdO1a9eax+uvv35atmz5ha93zpUw/vjHP+bTTz/9wte/1157pU2bNjWPF7TPWZCxY8dmiy22yIYbblgzrW3bttl3330XOH+XLl3Sr1+/WtNGjRqVXr16pU2bNrXei2233TazZs3K+PHjkyRrrLFGNt9884wcObLmuVOnTs2YMWOy7777zne7PYDFYe7P359++mmmTJmSbt26pXXr1rU+0/fq1SuTJk3KCy+8kOTzKyP17t07vXr1yoMPPpjk86soVSqVmm3uk08+mZdeein77LNPpkyZUrM9/PDDD7PNNttk/PjxNbdGa926dR599NG8+eabCxzn448/nsmTJ2fw4MFp2rRpzfSddtopa621Vv70pz/N95wf//jHNf+7devWWXPNNdO8efMMHDiwZvqaa66Z1q1b19o/1HU7DsCiq+v+Z27jxo3LySefnCOPPDL777//Iq979913r3XFialTp+Yvf/lLBg4cmA8++KBmuz9lypT069cvL7300ny3DQXgi30bt/N77rlnWrVqVfN48803T5Lst99+WWaZZWpN/+STT75wma1bt87rr7+exx57bFFeIsB3wped907qd+57xRVXrHU1oZYtW2bQoEF54okn8n//939JPr9KU9OmTWt9Z3/vvffmnXfeqfMdDgYNGpTllluu5vEee+yRFVZYoda4F5VjFxaVW7lR1Oqrr17rcdeuXdOgQYOa+w4//PDDOf300zNhwoR89NFHteadNm1arQ/Vq6666nzLb9OmTd59992ax7Nnz85ll12WYcOG5ZVXXsmsWbNq/rag2/DMu8w5J4znXuZrr72W0047LXfffXet6XPGCPBN8eqrr6Zr167zxSXdunVb4Pwrr7zyfPO2adMmTz/99CKtf/Lkyfn4448XuL6FjWHOLWzmePvtt/Pee+/lN7/5TX7zm98sdD3J55dBfe655xZ6Sek5881Rl/3MvPr06ZPdd989Q4YMySWXXJK+ffvmhz/8YfbZZ5/5brVWl33Ogrz66qvZYost5pte1/cs+fy9ePrpp+v0XgwaNChHHHFEXn311XTq1CmjRo3Kp59++pW+gAOoj48//jjnnntuRowYkTfeeCOVSqXmb3N//p4TGz344INZeeWV88QTT+RXv/pVOnTokF//+tc1f2vZsmU22GCDJJ9vD5PkgAMOWOj6p02bljZt2uSCCy7IAQcckFVWWSU9evTIjjvumEGDBmW11VZL8vn2Ofk8JJrXWmutlYceeqjWtKZNm863HW7VqtUC97etWrWqtX+oz3YcgEVT1/3PHK+//nr22muv9OzZMxdffPFXWve8n+FffvnlVCqVnHrqqTn11FMX+JzJkydnpZVW+krrBfgu+TZu5+f9rmnO+ZRVVlllgdO/6Duok046Kffff38222yzdOvWLdtvv3322Wef9OzZ8wvHAPBd8mXnvZP6nfvu1q3bfN8JrbHGGkmSiRMnpmPHjmndunV22WWX3HLLLTnrrLOSfH4bt5VWWqnmR9j1HXdVVVW6detWa9yLyrELi0qYxGI198b13//+d7bZZpustdZaufjii7PKKqukcePG+fOf/5xLLrmk5pfKczRs2HCBy5z7AOKcc87JqaeemoMPPjhnnXVW2rZtmwYNGuSYY46Zb3l1WeasWbOy3XbbZerUqTnppJOy1lprpXnz5nnjjTdy4IEHLnCZAN8WddnuLm7z3t94znZ3v/32W+hJ5fXXX79m3vXWW2+hXx7N+yXNorzeqqqqjB49On/7299yzz335N57783BBx+ciy66KH/729/SokWLr7T8RbGge0LPnj072223XU488cQFPmfOwU6S7L333jn22GMzcuTInHLKKbn55puzySabLPDEO8DicOSRR2bEiBE55phjssUWW6RVq1apqqrK3nvvXevz94orrpguXbpk/Pjx6dy5cyqVSrbYYot06NAhRx99dF599dU8+OCD2XLLLWuuqjrn+RdeeGGtK9HNbc62e+DAgenVq1fuvPPO3Hfffbnwwgtz/vnn5/e///1Cr/T3RRa2H6jL/qE+23EAFk1d9z9J8sknn2SPPfZIkyZNcvvtt9e6KkXy+XHCgj7nz/0Durkt7Ljn+OOPn+9qqHMs7IcKACzYt3E7/1WOMea19tpr54UXXsgf//jHjB07NnfccUeGDRuW0047LUOGDPnSsQB8F80bFdX33HddDRo0KKNGjcojjzyS9dZbL3fffXcGDx5c833X4rCwuyfMu69z7MKiEiZR1EsvvVTr1wAvv/xyZs+enc6dO+eee+7JzJkzc/fdd9cq++e9tU59jB49Ot///vdz3XXX1Zr+3nvvpX379vVe3jPPPJMXX3wxv/3tbzNo0KCa6f/zP/+zyGMEWFI6deqUZ599NpVKpdaHypdffnmRl1mfW3tVV1enadOmC1xfXcfQoUOHLLfccpk1a1a23XbbL5y3a9eueeqpp7LNNtss9luQfe9738v3vve9nH322bnllluy77775ne/+12tW/Ysqk6dOn2l9yz5/L2YPn36l75nyee3idtpp50ycuTI7Lvvvnn44Ydz6aWX1mfIAF/J6NGjc8ABB+Siiy6qmTZjxoy89957883bq1evjB8/Pl26dMmGG26Y5ZZbLhtssEFatWqVsWPH5h//+EetL9Hn3LKzZcuWddomrrDCChk8eHAGDx6cyZMnZ+ONN87ZZ5+d/v37p1OnTkmSF154Yb5fqL3wwgs1fy+hPttxABZNffY/Rx11VJ588smMHz8+yy+//Hx/b9OmzQJv2TznantfZs7V+Ro1amTbD1CI7fyXa968efbaa6/stdde+eSTT7Lbbrvl7LPPzsknn5ymTZsu9u/XAJZ2X3TeO0m9z33PudrQ3NvXF198MUlqlpkkO+ywQzp06JCRI0dm8803z0cffVSvOxzMuYL4HJVKJS+//HLNj7wXZM4dH+bdT867r1ta92ks/RZfVsd30pVXXlnr8dChQ5Mk/fv3r6n2571k6ogRIxZ5fQ0bNpyv+h81atQi37tyQWOsVCq57LLLFnmMAEtKv3798sYbb+Tuu++umTZjxoxcc801i7zM5s2bJ5n/w+mCNGzYMNtuu23uuuuuvPnmmzXTX3755YwZM6ZO62vYsGF233333HHHHfnnP/8539/ffvvtmv89cODAvPHGGwt8fR9//HE+/PDDOq3zi7z77rvz7XfmXIFj5syZX3n5yef/bhMmTMiTTz5ZM23q1Km17in9ZQYOHJgJEybk3nvvne9v7733Xj777LNa0/bff/88++yzOeGEE9KwYcPsvffeizx+gPpa0Gf6oUOHLvDXx7169crEiRNz22231dzarUGDBtlyyy1z8cUX59NPP62ZniQ9evRI165d8+tf/zrTp0+fb3lz9iOzZs2a73YO1dXVWXHFFWu275tsskmqq6tz9dVX19rmjxkzJs8991x22mmnRXwH5lff7TgA9VfX/c+IESMyfPjwXHnlldlss80WuKyuXbvm+eefr3V88tRTT+Xhhx+u01iqq6vTt2/fDB8+PG+99dZ8f597uQDUje38F5syZUqtx40bN0737t1TqVTy6aefJqnf94AA30ZfdN47WfB55S869/3mm2/mzjvvrHn8/vvv58Ybb8yGG26Yjh071kxfZpll8qMf/Si33357brjhhqy33npfGBXN68Ybb8wHH3xQ83j06NF56623vvCK4J06dUrDhg0zfvz4WtOHDRtW6/HSuE/jm8EVkyjqlVdeyYABA7LDDjtkwoQJufnmm7PPPvtkgw02SNOmTdO4cePssssuOeywwzJ9+vRcc801qa6uXuCGqy523nnnnHnmmTnooIOy5ZZb5plnnsnIkSNras36WmuttdK1a9ccf/zxeeONN9KyZcvccccdX3gvZoCl1WGHHZYrrrg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      "text/plain": [
       "<Figure size 3000x3000 with 32 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def one_hot_tokens_to_str(tokens):\n",
    "    # print(\"see\")\n",
    "    name_str=\"\"\n",
    "    for idx,token in enumerate(tokens):\n",
    "        token_idx = np.argmax(token)\n",
    "        if(token.sum()==0):    continue   # skip padding tokens\n",
    "        name_str+=idx_to_token[token_idx]\n",
    "    return name_str\n",
    "\n",
    "def plot_single_example(name_str,color):\n",
    "    plt.axis(\"off\")\n",
    "    plt.title(name_str)\n",
    "    plt.imshow(color.reshape([1,1,3]))\n",
    "\n",
    "\n",
    "\n",
    "for data in train_ds.take(1):\n",
    "    tokens=data[0].numpy()\n",
    "    colors=data[1].numpy()\n",
    "    \n",
    "    cols=6\n",
    "    rows=math.ceil(len(tokens)/cols)\n",
    "    size=5\n",
    "\n",
    "    fig=plt.figure(figsize=(size*rows,size*cols))\n",
    "\n",
    "    for idx in range(len(tokens)):\n",
    "        name_str=one_hot_tokens_to_str(tokens[idx])\n",
    "        color=(colors[idx]*255).astype(\"uint8\")\n",
    "        fig.add_subplot(rows,cols,idx+1)    \n",
    "        plot_single_example(name_str,color)\n",
    "    \n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6c28305",
   "metadata": {},
   "source": [
    "## Defining an Simple Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "47a516d9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"Colgen-1\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_1 (InputLayer)        [(None, None, 37)]        0         \n",
      "                                                                 \n",
      " gru (GRU)                   (None, None, 32)          6816      \n",
      "                                                                 \n",
      " gru_1 (GRU)                 (None, None, 64)          18816     \n",
      "                                                                 \n",
      " gru_2 (GRU)                 (None, 128)               74496     \n",
      "                                                                 \n",
      " dense (Dense)               (None, 64)                8256      \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 32)                2080      \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 3)                 99        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 110563 (431.89 KB)\n",
      "Trainable params: 110563 (431.89 KB)\n",
      "Non-trainable params: 0 (0.00 Byte)\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras import layers,Model\n",
    "\n",
    "def get_colgen_1():\n",
    "    x_input = layers.Input(shape=(None,len(TOKENS)))\n",
    "    x = layers.GRU(units=32,return_sequences=True)(x_input)\n",
    "    x = layers.GRU(units=64,return_sequences=True)(x)\n",
    "    x = layers.GRU(units=128,return_sequences=False)(x)\n",
    "    x = layers.Dense(64,activation='relu')(x)\n",
    "    x = layers.Dense(32,activation='relu')(x)\n",
    "    x = layers.Dense(3,activation='sigmoid')(x)\n",
    "    model = Model(x_input,x,name=\"Colgen-1\")\n",
    "    return model\n",
    "\n",
    "model = get_colgen_1()\n",
    "model.summary()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc3af968",
   "metadata": {},
   "source": [
    "## Compiling the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "5142f0db",
   "metadata": {},
   "outputs": [],
   "source": [
    "def euclidean_distance(y_true,y_pred):\n",
    "    '''\n",
    "        y_true shape:(batch_size,3)\n",
    "        y_pred shape:(batch_size,3)\n",
    "    '''\n",
    "\n",
    "    return tf.reduce_mean(tf.sqrt(tf.reduce_sum(tf.square(y_true-y_pred),axis=-1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "d4d2ad0b",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = get_colgen_1()\n",
    "model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),loss=\"MSE\",metrics=[\"acc\",euclidean_distance])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a12b736",
   "metadata": {},
   "source": [
    "## Training the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "5eeb102d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "24/24 [==============================] - 11s 88ms/step - loss: 0.0996 - acc: 0.5942 - euclidean_distance: 0.5166 - val_loss: 0.1138 - val_acc: 0.5278 - val_euclidean_distance: 0.5541\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 1s 41ms/step - loss: 0.0923 - acc: 0.5981 - euclidean_distance: 0.4938 - val_loss: 0.1038 - val_acc: 0.6597 - val_euclidean_distance: 0.5208\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 1s 41ms/step - loss: 0.0854 - acc: 0.6817 - euclidean_distance: 0.4724 - val_loss: 0.1002 - val_acc: 0.7222 - val_euclidean_distance: 0.5050\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 1s 41ms/step - loss: 0.0776 - acc: 0.7188 - euclidean_distance: 0.4466 - val_loss: 0.0907 - val_acc: 0.7222 - val_euclidean_distance: 0.4776\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 1s 43ms/step - loss: 0.0729 - acc: 0.7334 - euclidean_distance: 0.4283 - val_loss: 0.0876 - val_acc: 0.7083 - val_euclidean_distance: 0.4671\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 1s 41ms/step - loss: 0.0693 - acc: 0.7308 - euclidean_distance: 0.4213 - val_loss: 0.0787 - val_acc: 0.7500 - val_euclidean_distance: 0.4415\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 1s 42ms/step - loss: 0.0669 - acc: 0.7427 - euclidean_distance: 0.4085 - val_loss: 0.0756 - val_acc: 0.7431 - val_euclidean_distance: 0.4274\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 1s 42ms/step - loss: 0.0644 - acc: 0.7573 - euclidean_distance: 0.3996 - val_loss: 0.0794 - val_acc: 0.7292 - val_euclidean_distance: 0.4420\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 1s 41ms/step - loss: 0.0646 - acc: 0.7560 - euclidean_distance: 0.4025 - val_loss: 0.0764 - val_acc: 0.7778 - val_euclidean_distance: 0.4304\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 1s 40ms/step - loss: 0.0618 - acc: 0.7599 - euclidean_distance: 0.3922 - val_loss: 0.0741 - val_acc: 0.7639 - val_euclidean_distance: 0.4226\n",
      "Epoch 11/100\n",
      "24/24 [==============================] - 1s 42ms/step - loss: 0.0607 - acc: 0.7745 - euclidean_distance: 0.3873 - val_loss: 0.0752 - val_acc: 0.7917 - val_euclidean_distance: 0.4290\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 1s 42ms/step - loss: 0.0591 - acc: 0.7785 - euclidean_distance: 0.3821 - val_loss: 0.0689 - val_acc: 0.8125 - val_euclidean_distance: 0.4060\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 1s 41ms/step - loss: 0.0570 - acc: 0.7905 - euclidean_distance: 0.3736 - val_loss: 0.0685 - val_acc: 0.8056 - val_euclidean_distance: 0.4044\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 1s 41ms/step - loss: 0.0567 - acc: 0.7905 - euclidean_distance: 0.3732 - val_loss: 0.0688 - val_acc: 0.7847 - val_euclidean_distance: 0.4047\n",
      "Epoch 15/100\n",
      "24/24 [==============================] - 1s 42ms/step - loss: 0.0540 - acc: 0.7891 - euclidean_distance: 0.3641 - val_loss: 0.0646 - val_acc: 0.7639 - val_euclidean_distance: 0.3900\n",
      "Epoch 16/100\n",
      "24/24 [==============================] - 1s 42ms/step - loss: 0.0529 - acc: 0.7984 - euclidean_distance: 0.3597 - val_loss: 0.0675 - val_acc: 0.8333 - val_euclidean_distance: 0.3993\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 1s 42ms/step - loss: 0.0508 - acc: 0.8024 - euclidean_distance: 0.3528 - val_loss: 0.0625 - val_acc: 0.8056 - val_euclidean_distance: 0.3778\n",
      "Epoch 18/100\n",
      "24/24 [==============================] - 1s 41ms/step - loss: 0.0500 - acc: 0.8011 - euclidean_distance: 0.3470 - val_loss: 0.0593 - val_acc: 0.8056 - val_euclidean_distance: 0.3716\n",
      "Epoch 19/100\n",
      "24/24 [==============================] - 1s 41ms/step - loss: 0.0480 - acc: 0.8103 - euclidean_distance: 0.3406 - val_loss: 0.0581 - val_acc: 0.8194 - val_euclidean_distance: 0.3642\n",
      "Epoch 20/100\n",
      "24/24 [==============================] - 1s 41ms/step - loss: 0.0458 - acc: 0.8050 - euclidean_distance: 0.3329 - val_loss: 0.0566 - val_acc: 0.8264 - val_euclidean_distance: 0.3617\n",
      "Epoch 21/100\n",
      "24/24 [==============================] - 1s 41ms/step - loss: 0.0456 - acc: 0.7971 - euclidean_distance: 0.3286 - val_loss: 0.0575 - val_acc: 0.7847 - val_euclidean_distance: 0.3649\n",
      "Epoch 22/100\n",
      "24/24 [==============================] - 1s 42ms/step - loss: 0.0433 - acc: 0.8024 - euclidean_distance: 0.3215 - val_loss: 0.0535 - val_acc: 0.7778 - val_euclidean_distance: 0.3500\n",
      "Epoch 23/100\n",
      "24/24 [==============================] - 1s 41ms/step - loss: 0.0416 - acc: 0.8117 - euclidean_distance: 0.3141 - val_loss: 0.0515 - val_acc: 0.7778 - val_euclidean_distance: 0.3409\n",
      "Epoch 24/100\n",
      "24/24 [==============================] - 1s 42ms/step - loss: 0.0402 - acc: 0.8011 - euclidean_distance: 0.3086 - val_loss: 0.0508 - val_acc: 0.8056 - val_euclidean_distance: 0.3394\n",
      "Epoch 25/100\n",
      "24/24 [==============================] - 1s 41ms/step - loss: 0.0383 - acc: 0.8210 - euclidean_distance: 0.2987 - val_loss: 0.0511 - val_acc: 0.8194 - val_euclidean_distance: 0.3436\n",
      "Epoch 26/100\n",
      "24/24 [==============================] - 1s 41ms/step - loss: 0.0373 - acc: 0.8077 - euclidean_distance: 0.2962 - val_loss: 0.0489 - val_acc: 0.8056 - val_euclidean_distance: 0.3321\n",
      "Epoch 27/100\n",
      "24/24 [==============================] - 1s 42ms/step - loss: 0.0362 - acc: 0.8223 - euclidean_distance: 0.2926 - val_loss: 0.0453 - val_acc: 0.7847 - val_euclidean_distance: 0.3182\n",
      "Epoch 28/100\n",
      "24/24 [==============================] - 1s 41ms/step - loss: 0.0350 - acc: 0.8329 - euclidean_distance: 0.2866 - val_loss: 0.0468 - val_acc: 0.7917 - val_euclidean_distance: 0.3211\n",
      "Epoch 29/100\n",
      "24/24 [==============================] - 1s 41ms/step - loss: 0.0338 - acc: 0.8156 - euclidean_distance: 0.2814 - val_loss: 0.0419 - val_acc: 0.7708 - val_euclidean_distance: 0.3086\n",
      "Epoch 30/100\n",
      "24/24 [==============================] - 1s 41ms/step - loss: 0.0317 - acc: 0.8369 - euclidean_distance: 0.2716 - val_loss: 0.0412 - val_acc: 0.7917 - val_euclidean_distance: 0.3048\n",
      "Epoch 31/100\n",
      "24/24 [==============================] - 1s 42ms/step - loss: 0.0303 - acc: 0.8263 - euclidean_distance: 0.2674 - val_loss: 0.0400 - val_acc: 0.8056 - val_euclidean_distance: 0.3023\n",
      "Epoch 32/100\n",
      "24/24 [==============================] - 1s 42ms/step - loss: 0.0280 - acc: 0.8382 - euclidean_distance: 0.2571 - val_loss: 0.0372 - val_acc: 0.8125 - val_euclidean_distance: 0.2888\n",
      "Epoch 33/100\n",
      "24/24 [==============================] - 1s 42ms/step - loss: 0.0269 - acc: 0.8528 - euclidean_distance: 0.2498 - val_loss: 0.0358 - val_acc: 0.7917 - val_euclidean_distance: 0.2806\n",
      "Epoch 34/100\n",
      "24/24 [==============================] - 1s 41ms/step - loss: 0.0258 - acc: 0.8408 - euclidean_distance: 0.2452 - val_loss: 0.0342 - val_acc: 0.7986 - val_euclidean_distance: 0.2685\n",
      "Epoch 35/100\n",
      "24/24 [==============================] - 1s 41ms/step - loss: 0.0249 - acc: 0.8448 - euclidean_distance: 0.2428 - val_loss: 0.0334 - val_acc: 0.7847 - val_euclidean_distance: 0.2794\n",
      "Epoch 36/100\n",
      "24/24 [==============================] - 1s 41ms/step - loss: 0.0237 - acc: 0.8448 - euclidean_distance: 0.2367 - val_loss: 0.0322 - val_acc: 0.8056 - val_euclidean_distance: 0.2630\n",
      "Epoch 37/100\n",
      "24/24 [==============================] - 1s 41ms/step - loss: 0.0220 - acc: 0.8501 - euclidean_distance: 0.2273 - val_loss: 0.0309 - val_acc: 0.7986 - val_euclidean_distance: 0.2587\n",
      "Epoch 38/100\n",
      "24/24 [==============================] - 1s 42ms/step - loss: 0.0221 - acc: 0.8382 - euclidean_distance: 0.2261 - val_loss: 0.0299 - val_acc: 0.8264 - val_euclidean_distance: 0.2512\n",
      "Epoch 39/100\n",
      "24/24 [==============================] - 1s 41ms/step - loss: 0.0201 - acc: 0.8528 - euclidean_distance: 0.2163 - val_loss: 0.0311 - val_acc: 0.8194 - val_euclidean_distance: 0.2526\n",
      "Epoch 40/100\n",
      "24/24 [==============================] - 1s 43ms/step - loss: 0.0190 - acc: 0.8541 - euclidean_distance: 0.2117 - val_loss: 0.0280 - val_acc: 0.8194 - val_euclidean_distance: 0.2405\n",
      "Epoch 41/100\n",
      "24/24 [==============================] - 1s 43ms/step - loss: 0.0174 - acc: 0.8475 - euclidean_distance: 0.2012 - val_loss: 0.0283 - val_acc: 0.8194 - val_euclidean_distance: 0.2310\n",
      "Epoch 42/100\n",
      "24/24 [==============================] - 1s 41ms/step - loss: 0.0168 - acc: 0.8528 - euclidean_distance: 0.1993 - val_loss: 0.0285 - val_acc: 0.8056 - val_euclidean_distance: 0.2484\n",
      "Epoch 43/100\n",
      "24/24 [==============================] - 1s 40ms/step - loss: 0.0162 - acc: 0.8435 - euclidean_distance: 0.1952 - val_loss: 0.0300 - val_acc: 0.8056 - val_euclidean_distance: 0.2506\n",
      "Epoch 44/100\n",
      "24/24 [==============================] - 1s 40ms/step - loss: 0.0155 - acc: 0.8714 - euclidean_distance: 0.1905 - val_loss: 0.0260 - val_acc: 0.7986 - val_euclidean_distance: 0.2254\n",
      "Epoch 45/100\n",
      "24/24 [==============================] - 1s 40ms/step - loss: 0.0136 - acc: 0.8687 - euclidean_distance: 0.1763 - val_loss: 0.0240 - val_acc: 0.8194 - val_euclidean_distance: 0.2122\n",
      "Epoch 46/100\n",
      "24/24 [==============================] - 1s 40ms/step - loss: 0.0123 - acc: 0.8700 - euclidean_distance: 0.1674 - val_loss: 0.0244 - val_acc: 0.8333 - val_euclidean_distance: 0.2127\n",
      "Epoch 47/100\n",
      "24/24 [==============================] - 1s 41ms/step - loss: 0.0116 - acc: 0.8833 - euclidean_distance: 0.1618 - val_loss: 0.0236 - val_acc: 0.8125 - val_euclidean_distance: 0.2029\n",
      "Epoch 48/100\n",
      "24/24 [==============================] - 1s 40ms/step - loss: 0.0107 - acc: 0.8846 - euclidean_distance: 0.1557 - val_loss: 0.0231 - val_acc: 0.7778 - val_euclidean_distance: 0.1985\n",
      "Epoch 49/100\n",
      "24/24 [==============================] - 1s 41ms/step - loss: 0.0104 - acc: 0.8767 - euclidean_distance: 0.1538 - val_loss: 0.0228 - val_acc: 0.7986 - val_euclidean_distance: 0.2025\n",
      "Epoch 50/100\n",
      "24/24 [==============================] - 1s 41ms/step - loss: 0.0096 - acc: 0.8806 - euclidean_distance: 0.1470 - val_loss: 0.0211 - val_acc: 0.8056 - val_euclidean_distance: 0.1901\n",
      "Epoch 51/100\n",
      "24/24 [==============================] - 1s 40ms/step - loss: 0.0088 - acc: 0.8820 - euclidean_distance: 0.1409 - val_loss: 0.0203 - val_acc: 0.7847 - val_euclidean_distance: 0.1821\n",
      "Epoch 52/100\n",
      "24/24 [==============================] - 1s 43ms/step - loss: 0.0083 - acc: 0.8753 - euclidean_distance: 0.1372 - val_loss: 0.0209 - val_acc: 0.7847 - val_euclidean_distance: 0.1828\n",
      "Epoch 53/100\n",
      "24/24 [==============================] - 1s 44ms/step - loss: 0.0075 - acc: 0.8846 - euclidean_distance: 0.1289 - val_loss: 0.0207 - val_acc: 0.7639 - val_euclidean_distance: 0.1841\n",
      "Epoch 54/100\n",
      "24/24 [==============================] - 1s 42ms/step - loss: 0.0074 - acc: 0.8952 - euclidean_distance: 0.1285 - val_loss: 0.0208 - val_acc: 0.7778 - val_euclidean_distance: 0.1851\n",
      "Epoch 55/100\n",
      "24/24 [==============================] - 1s 40ms/step - loss: 0.0069 - acc: 0.8846 - euclidean_distance: 0.1242 - val_loss: 0.0198 - val_acc: 0.7847 - val_euclidean_distance: 0.1757\n",
      "Epoch 56/100\n",
      "24/24 [==============================] - 1s 40ms/step - loss: 0.0068 - acc: 0.8780 - euclidean_distance: 0.1240 - val_loss: 0.0205 - val_acc: 0.8125 - val_euclidean_distance: 0.1741\n",
      "Epoch 57/100\n",
      "24/24 [==============================] - 1s 41ms/step - loss: 0.0062 - acc: 0.8846 - euclidean_distance: 0.1172 - val_loss: 0.0212 - val_acc: 0.7847 - val_euclidean_distance: 0.1772\n",
      "Epoch 58/100\n",
      "24/24 [==============================] - 1s 41ms/step - loss: 0.0063 - acc: 0.8806 - euclidean_distance: 0.1179 - val_loss: 0.0210 - val_acc: 0.8125 - val_euclidean_distance: 0.1775\n",
      "Epoch 59/100\n",
      "24/24 [==============================] - 1s 41ms/step - loss: 0.0061 - acc: 0.8873 - euclidean_distance: 0.1171 - val_loss: 0.0196 - val_acc: 0.7986 - val_euclidean_distance: 0.1705\n",
      "Epoch 60/100\n",
      "24/24 [==============================] - 1s 40ms/step - loss: 0.0058 - acc: 0.8966 - euclidean_distance: 0.1132 - val_loss: 0.0204 - val_acc: 0.7569 - val_euclidean_distance: 0.1698\n",
      "Epoch 61/100\n",
      "24/24 [==============================] - 1s 41ms/step - loss: 0.0055 - acc: 0.8806 - euclidean_distance: 0.1109 - val_loss: 0.0202 - val_acc: 0.7847 - val_euclidean_distance: 0.1662\n",
      "Epoch 62/100\n",
      "24/24 [==============================] - 1s 42ms/step - loss: 0.0052 - acc: 0.8833 - euclidean_distance: 0.1069 - val_loss: 0.0192 - val_acc: 0.8056 - val_euclidean_distance: 0.1607\n",
      "Epoch 63/100\n",
      "24/24 [==============================] - 1s 43ms/step - loss: 0.0047 - acc: 0.8966 - euclidean_distance: 0.1021 - val_loss: 0.0201 - val_acc: 0.7917 - val_euclidean_distance: 0.1653\n",
      "Epoch 64/100\n",
      "24/24 [==============================] - 1s 42ms/step - loss: 0.0046 - acc: 0.8952 - euclidean_distance: 0.1000 - val_loss: 0.0190 - val_acc: 0.8056 - val_euclidean_distance: 0.1592\n",
      "Epoch 65/100\n",
      "24/24 [==============================] - 1s 40ms/step - loss: 0.0046 - acc: 0.9058 - euclidean_distance: 0.1014 - val_loss: 0.0201 - val_acc: 0.7569 - val_euclidean_distance: 0.1628\n",
      "Epoch 66/100\n",
      "24/24 [==============================] - 1s 41ms/step - loss: 0.0047 - acc: 0.8780 - euclidean_distance: 0.1025 - val_loss: 0.0197 - val_acc: 0.7500 - val_euclidean_distance: 0.1638\n",
      "Epoch 67/100\n",
      "24/24 [==============================] - 1s 43ms/step - loss: 0.0048 - acc: 0.8833 - euclidean_distance: 0.1035 - val_loss: 0.0205 - val_acc: 0.8125 - val_euclidean_distance: 0.1636\n",
      "Epoch 68/100\n",
      "24/24 [==============================] - 1s 42ms/step - loss: 0.0050 - acc: 0.8886 - euclidean_distance: 0.1067 - val_loss: 0.0210 - val_acc: 0.8056 - val_euclidean_distance: 0.1683\n",
      "Epoch 69/100\n",
      "24/24 [==============================] - 1s 42ms/step - loss: 0.0047 - acc: 0.8992 - euclidean_distance: 0.1021 - val_loss: 0.0203 - val_acc: 0.7708 - val_euclidean_distance: 0.1662\n",
      "Epoch 70/100\n",
      "24/24 [==============================] - 1s 42ms/step - loss: 0.0046 - acc: 0.8912 - euclidean_distance: 0.1018 - val_loss: 0.0188 - val_acc: 0.7986 - val_euclidean_distance: 0.1544\n",
      "Epoch 71/100\n",
      "24/24 [==============================] - 1s 41ms/step - loss: 0.0046 - acc: 0.8966 - euclidean_distance: 0.1013 - val_loss: 0.0189 - val_acc: 0.7778 - val_euclidean_distance: 0.1603\n",
      "Epoch 72/100\n",
      "24/24 [==============================] - 1s 41ms/step - loss: 0.0043 - acc: 0.8873 - euclidean_distance: 0.0989 - val_loss: 0.0193 - val_acc: 0.7708 - val_euclidean_distance: 0.1556\n",
      "Epoch 73/100\n",
      "24/24 [==============================] - 1s 40ms/step - loss: 0.0035 - acc: 0.8952 - euclidean_distance: 0.0892 - val_loss: 0.0182 - val_acc: 0.7708 - val_euclidean_distance: 0.1469\n",
      "Epoch 74/100\n",
      "24/24 [==============================] - 1s 42ms/step - loss: 0.0033 - acc: 0.8926 - euclidean_distance: 0.0860 - val_loss: 0.0190 - val_acc: 0.7708 - val_euclidean_distance: 0.1526\n",
      "Epoch 75/100\n",
      "24/24 [==============================] - 1s 41ms/step - loss: 0.0033 - acc: 0.8833 - euclidean_distance: 0.0865 - val_loss: 0.0188 - val_acc: 0.7708 - val_euclidean_distance: 0.1492\n",
      "Epoch 76/100\n",
      "24/24 [==============================] - 1s 41ms/step - loss: 0.0029 - acc: 0.8966 - euclidean_distance: 0.0816 - val_loss: 0.0186 - val_acc: 0.7361 - val_euclidean_distance: 0.1506\n",
      "Epoch 77/100\n",
      "24/24 [==============================] - 1s 41ms/step - loss: 0.0032 - acc: 0.9032 - euclidean_distance: 0.0859 - val_loss: 0.0186 - val_acc: 0.7917 - val_euclidean_distance: 0.1477\n",
      "Epoch 78/100\n",
      "24/24 [==============================] - 1s 40ms/step - loss: 0.0031 - acc: 0.9019 - euclidean_distance: 0.0844 - val_loss: 0.0191 - val_acc: 0.7500 - val_euclidean_distance: 0.1515\n",
      "Epoch 79/100\n",
      "24/24 [==============================] - 1s 40ms/step - loss: 0.0031 - acc: 0.8806 - euclidean_distance: 0.0859 - val_loss: 0.0181 - val_acc: 0.7708 - val_euclidean_distance: 0.1462\n",
      "Epoch 80/100\n",
      "24/24 [==============================] - 1s 41ms/step - loss: 0.0030 - acc: 0.8992 - euclidean_distance: 0.0823 - val_loss: 0.0182 - val_acc: 0.7639 - val_euclidean_distance: 0.1444\n",
      "Epoch 81/100\n",
      "24/24 [==============================] - 1s 41ms/step - loss: 0.0030 - acc: 0.9032 - euclidean_distance: 0.0809 - val_loss: 0.0193 - val_acc: 0.7500 - val_euclidean_distance: 0.1462\n",
      "Epoch 82/100\n",
      "24/24 [==============================] - 1s 41ms/step - loss: 0.0030 - acc: 0.9085 - euclidean_distance: 0.0817 - val_loss: 0.0188 - val_acc: 0.7361 - val_euclidean_distance: 0.1483\n",
      "Epoch 83/100\n",
      "24/24 [==============================] - 1s 41ms/step - loss: 0.0027 - acc: 0.9032 - euclidean_distance: 0.0783 - val_loss: 0.0180 - val_acc: 0.7847 - val_euclidean_distance: 0.1458\n",
      "Epoch 84/100\n",
      "24/24 [==============================] - 1s 40ms/step - loss: 0.0026 - acc: 0.9005 - euclidean_distance: 0.0777 - val_loss: 0.0181 - val_acc: 0.7986 - val_euclidean_distance: 0.1479\n",
      "Epoch 85/100\n",
      "24/24 [==============================] - 1s 41ms/step - loss: 0.0027 - acc: 0.9204 - euclidean_distance: 0.0784 - val_loss: 0.0186 - val_acc: 0.7500 - val_euclidean_distance: 0.1445\n",
      "Epoch 86/100\n",
      "24/24 [==============================] - 1s 41ms/step - loss: 0.0028 - acc: 0.9045 - euclidean_distance: 0.0785 - val_loss: 0.0183 - val_acc: 0.7639 - val_euclidean_distance: 0.1450\n",
      "Epoch 87/100\n",
      "24/24 [==============================] - 1s 42ms/step - loss: 0.0027 - acc: 0.9019 - euclidean_distance: 0.0782 - val_loss: 0.0173 - val_acc: 0.7847 - val_euclidean_distance: 0.1428\n",
      "Epoch 88/100\n",
      "24/24 [==============================] - 1s 41ms/step - loss: 0.0028 - acc: 0.9072 - euclidean_distance: 0.0808 - val_loss: 0.0185 - val_acc: 0.7847 - val_euclidean_distance: 0.1447\n",
      "Epoch 89/100\n",
      "24/24 [==============================] - 1s 40ms/step - loss: 0.0030 - acc: 0.8939 - euclidean_distance: 0.0838 - val_loss: 0.0186 - val_acc: 0.7500 - val_euclidean_distance: 0.1438\n",
      "Epoch 90/100\n",
      "24/24 [==============================] - 1s 41ms/step - loss: 0.0028 - acc: 0.9178 - euclidean_distance: 0.0791 - val_loss: 0.0185 - val_acc: 0.8125 - val_euclidean_distance: 0.1443\n",
      "Epoch 91/100\n",
      "24/24 [==============================] - 1s 41ms/step - loss: 0.0026 - acc: 0.8992 - euclidean_distance: 0.0750 - val_loss: 0.0182 - val_acc: 0.7639 - val_euclidean_distance: 0.1462\n",
      "Epoch 92/100\n",
      "24/24 [==============================] - 1s 41ms/step - loss: 0.0026 - acc: 0.8966 - euclidean_distance: 0.0766 - val_loss: 0.0184 - val_acc: 0.7986 - val_euclidean_distance: 0.1419\n",
      "Epoch 93/100\n",
      "24/24 [==============================] - 1s 41ms/step - loss: 0.0028 - acc: 0.9072 - euclidean_distance: 0.0783 - val_loss: 0.0185 - val_acc: 0.7917 - val_euclidean_distance: 0.1497\n",
      "Epoch 94/100\n",
      "24/24 [==============================] - 1s 40ms/step - loss: 0.0030 - acc: 0.9164 - euclidean_distance: 0.0817 - val_loss: 0.0177 - val_acc: 0.8056 - val_euclidean_distance: 0.1462\n",
      "Epoch 95/100\n",
      "24/24 [==============================] - 1s 41ms/step - loss: 0.0029 - acc: 0.9032 - euclidean_distance: 0.0807 - val_loss: 0.0178 - val_acc: 0.7917 - val_euclidean_distance: 0.1455\n",
      "Epoch 96/100\n",
      "24/24 [==============================] - 1s 42ms/step - loss: 0.0029 - acc: 0.9072 - euclidean_distance: 0.0802 - val_loss: 0.0181 - val_acc: 0.7986 - val_euclidean_distance: 0.1432\n",
      "Epoch 97/100\n",
      "24/24 [==============================] - 1s 40ms/step - loss: 0.0026 - acc: 0.9125 - euclidean_distance: 0.0774 - val_loss: 0.0193 - val_acc: 0.7569 - val_euclidean_distance: 0.1494\n",
      "Epoch 98/100\n",
      "24/24 [==============================] - 1s 41ms/step - loss: 0.0023 - acc: 0.9151 - euclidean_distance: 0.0724 - val_loss: 0.0184 - val_acc: 0.7431 - val_euclidean_distance: 0.1418\n",
      "Epoch 99/100\n",
      "24/24 [==============================] - 1s 42ms/step - loss: 0.0020 - acc: 0.9138 - euclidean_distance: 0.0665 - val_loss: 0.0188 - val_acc: 0.7569 - val_euclidean_distance: 0.1413\n",
      "Epoch 100/100\n",
      "24/24 [==============================] - 1s 41ms/step - loss: 0.0022 - acc: 0.9138 - euclidean_distance: 0.0688 - val_loss: 0.0178 - val_acc: 0.7986 - val_euclidean_distance: 0.1390\n"
     ]
    }
   ],
   "source": [
    "bs=32\n",
    "train_ds = get_tf_dataset(train_name_tokens,train_colors,bs=bs,shuffle=True)\n",
    "val_ds = get_tf_dataset(val_name_tokens,val_colors,bs=bs)\n",
    "history = model.fit(train_ds,validation_data=val_ds,epochs=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c7890bc",
   "metadata": {},
   "source": [
    "## Evaluating the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "1e3b7c54",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_history(hist,key):\n",
    "    epochs = list(range(1,len(hist[key])+1))\n",
    "    plt.figure()\n",
    "    plt.plot(epochs,hist[key] ,label=key)\n",
    "    plt.plot(epochs,hist[\"val_\"+key],label=\"val_\"+key)\n",
    "    plt.legend()\n",
    "    plt.xlabel(\"epochs\")\n",
    "    plt.ylabel(key)\n",
    "    plt.show()\n",
    "\n",
    "plot_history(history.history,\"loss\")\n",
    "plot_history(history.history,\"acc\")\n",
    "plot_history(history.history,\"euclidean_distance\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "ba282280",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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qrbj5bNeuXZOmTZumtRXmJPfff3+qbfXq1cmee+6Z/PCHP0y1Fc7333777VTbhg0bkrp166bNW4uzI3PPPn36pOU5xeVCSVL8vLdz585J69at0/KFgoKCpG3btsl+++23zRhnzZqVREQyceLEtPZHHnkkrX3z5s3J3nvvnZx66qlp/UaMGJFkZWUlH3zwQZIkSbJo0aKkfPnyyXXXXZfW74033kgqVKiQai9tDlacI444IsnPzy/SXtrc4Y477kgiIhkxYkSRMQpzmWXLlpU4Z+/QoUPSoUOHIu1bX8MkKfp53LBhQ9KqVaukU6dOae1b50EliYgkIpKXXnop1bZ48eIkJycnOeGEE1JthX9mTj/99LTtS3t9CvPXgw8+OFm/fn2q39ixY5OIKPb477nnniQikhdeeGG7xwEAwP/euHHj0tYskqTo3LYwD2nVqlWyYcOGVPvpp5+eZGVlJUcffXTamG3atEmbA5d2vlmS3/3ud0lEJJ9//nmR90ozt54/f35Srly55IQTTiiSGxTO9ZMkSVq2bFnsnLZwHr214s5dcbnngAEDksqVK6flZsXlCcUpXI+4+eabU23r169PDj744KR+/fqp61F4jZo2bZoWQ0FBQbLffvslXbt2TTvWtWvXJk2aNEl++tOfptp69uyZ5OTkJIsXL061zZs3Lylfvnyxx//JJ58kEZHccMMN2z0OAAC+XwrnyGeffXZa+wknnJDUqVMnra006zOrV68uUhuWJEly4403JllZWak56qpVq5KcnJzk8ssvT+s3aNCgpEqVKskXX3yxzbhLuwZU3FpMSd/Xb50/fZM6tSSx1mGt4/up3C6rzv2Oa9euXbzyyivx5ZdfRsTXPxdzzDHHxMEHH5x6VPKsWbMiKysr7fHLEREtWrSIdu3apV7Xq1cvDjjggPjggw92KpYpU6bEgQceGM2bN4/ly5en/uvUqVNERMycObPEbQufgjRjxowijyffWufOndMec33YYYdFRMRJJ50U1apVK9JeeDwvvfRSLF26NAYOHBg5OTmpft27d4/mzZvHP//5zyL7mjlzZnTt2jU6d+4cDzzwQGRnZ0dERJIkcf/998dxxx0XSZKkHW/Xrl1j9erV8corr5R4DJ999lkkSRK1atUq8t6WT8pat25dLF++PH7yk59ERKTGLCgoiGnTpsVxxx2XevT6lor7eaSdlZ2dHeXKff1HbvPmzbFixYqoWrVqHHDAAds8xu3p3bt32vU6+eSTY88994x//etfRfZ/1llnpbWV9rNWONagQYPStt/WU4Rr1aoVX3311XY/hwAAmWjrJ83+8pe/jIhIm8NtOZ9dvXp1LF++PDp06BAffPBBrF69Om37vfbaK+0prtWrV4/evXvHq6++Gv/9738j4uu7r3NycmLixImpfjNmzIjly5fHmWeeWaq4Szv33BmfffZZPPnkk9GrV6/4/PPPU3PTFStWRNeuXWP+/Pnx8ccfl7j9lClTokaNGvHTn/40bW6bn58fVatWTc1ty5UrFz/72c/ioYceis8//zy1/cSJE6Nt27bRpEmTiIh44IEHoqCgIHr16pU2XoMGDWK//fZLjbcjOdjWVqxYUWwuU9rc4f7774+6deumPj9b2pW5TET653HlypWxevXqVB69s9q0aRP5+fmp1/vuu28cf/zxMWPGjCI/NXveeeelvS7t9SnMX88777yoVKlSavu+ffuW+BThwmuyfPnynT42AAC+HXr37h0VK1ZMvT7ssMMiSZIiT0s97LDD4sMPP4xNmzZFROnnmyVZsWJFVKhQIfULhVsqzdx62rRpUVBQEFdffXUqNyi0O+f6hblYu3btYu3atfHOO+/s1JgVKlSIAQMGpF5XqlQpBgwYEEuXLo2XX345rW+fPn3SYnjttddi/vz5ccYZZ8SKFStS5/7LL7+Mzp07xzPPPBMFBQWxefPmmDFjRvTs2TP23Xff1PYHHnhgiT+ha64PAMDW3zW3a9cuVqxYEWvWrEm1lWZ9pnr16nH00UfH5MmTU08cjYiYNGlS/OQnP0nNUWvUqBHHH3983Hvvval+mzdvjkmTJkXPnj2jSpUq2425NGtA38Q3qVOLsNZhreP7qUJZB/Bt0a5du9i0aVM899xzsc8++8TSpUujXbt28dZbb6UV1bZo0SJq166dtu2WyXqhWrVqxcqVK3cqlvnz58fbb78d9erVK/b9pUuXlrhtkyZN4pJLLokRI0bExIkTo127dtGjR48488wzi/wh2jruwve3fiR0YXvh8SxevDgiIg444IAi+2/evHk8++yzaW3r1q2L7t27R35+fkyePDkqVPi/j92yZcti1apVMXbs2Bg7duwOH2+hLf+BKvTZZ5/F0KFD47777isyRuE/csuWLYs1a9YUedz27lBQUBAjR46MUaNGxcKFC9P+4qxTp85Oj7vffvulvc7KyopmzZrFokWL0tobNmyY9hdrROk/a4sXL45y5cpFXl5e2vvFfQYKFV6TXf0PHADA98HWc7i8vLwoV65c2hxu9uzZMXjw4HjuueeKFGuuXr06bX7frFmzIvOu/fffPyIiFi1aFA0aNIiaNWvGcccdF/fcc08MGzYsIr4uJG3YsGHqi5EdjbukuefOeP/99yNJkrjqqqviqquuKrbP0qVLo2HDhsW+N3/+/Fi9enXUr1+/xG0L9e7dO2644Yb4+9//Hr1794533303Xn755Rg9enTaeEmSFDnmQoUL8zuSgxWnuFymtLnDggUL4oADDkjLsXaX6dOnx7XXXhuvvfZarF+/PtX+Teb7xZ3b/fffP9auXRvLli2LBg0apNoLi50Llfb6FOavW/erWLFiNG3atNht5TIAAN8fO7IOUlBQEKtXr446deqUer65M0ozt16wYEGUK1cuWrRosdP7Ka233norfve738WTTz6ZVkgQEUVu6Cytvfbaq0hxwJY5auEDUCKKn+tHfF1sW5LVq1fH+vXr46uvvir2Gh1wwAHF3vxprg8AwNY5QmHh4cqVK6N69eoRUfr1mVNPPTWmTZsWzz33XLRt2zYWLFgQL7/8ctxyyy1p2/Tu3TsmTZoUs2bNivbt28fjjz8eS5YsiZ///Oelirk0a0DfxDepUytkrSOdtY7vPkW1/98hhxwSOTk58cwzz8S+++4b9evXj/333z/atWsXo0aNivXr18esWbPSKv8LlS9fvtgxi/sLozQKCgqidevWMWLEiGLf3/rLnq3dfPPN0bdv33jwwQfj0UcfjUGDBsXw4cPj+eefj7333nu7ce/q48nOzo5jjjkmHnzwwXjkkUfi2GOPTb1XUFAQERFnnnlmiV+QHHTQQSWOXbt27cjKyiq2gLlXr14xZ86c+PWvfx0HH3xwVK1aNQoKCqJbt26p/e5OW99tcP3118dVV10VZ599dgwbNixq164d5cqVi4suuuh/Es+Wd1sU+qaftW1ZuXJlVK5cudj9AgCQbuuEdsGCBdG5c+do3rx5jBgxIvbZZ5+oVKlS/Otf/4o//vGPOz1/7N27d0yZMiXmzJkTrVu3joceeigGDhxY5MlHu1JJyfrW8+XCY7r00ktLfKpQs2bNStxPQUFB1K9fP+1JvFva8sugFi1aRH5+fkyYMCF69+4dEyZMiEqVKkWvXr3SxsvKyoqHH3642BxpyydOlTYH21qdOnWKzWX+V7lDVlZWsXne1tdm1qxZ0aNHj2jfvn2MGjUq9txzz6hYsWKMGzcu7rnnnl0Wz7ZsnVfsyPXZUYXXpG7dujs9BgAA3w47uw7yTeebderUiU2bNsXnn3+e9msf/6u5dWnzsFWrVkWHDh2ievXqcc0110ReXl7k5OTEK6+8EpdffnmZrF0U7vOmm26Kgw8+uNhtqlatmrYAXlrm+gAAbC8X2JH1meOOOy4qV64ckydPjrZt28bkyZOjXLlyccopp6SN3bVr19hjjz1iwoQJ0b59+5gwYUI0aNAgjjrqqN13oLHtvGDL8/BNa4esdZSetY7vDkW1/1+lSpXi0EMPjVmzZsW+++4b7dq1i4ivn2C7fv36mDhxYixZsiTat2+/y/ZZ0l9eeXl5MXfu3OjcufNOV4u3bt06WrduHb/73e9izpw5cfjhh8fo0aPj2muv/SYhR0REo0aNIiLi3XffLfJEq3fffTf1fqGsrKyYOHFiHH/88XHKKafEww8/HB07doyIrxe2q1WrFps3b96pfywqVKgQeXl5sXDhwrT2lStXxhNPPBFDhw6Nq6++OtVeeIdzoXr16kX16tXjzTff3OZ+tnUdatWqFatWrUpr27BhQ3z66adpbVOnTo0jjzwy/va3v6W1r1q16hv9Bbb1MSVJEu+///42i5ELlfaz1qhRoygoKEjdHVLo3XffLXGbhQsXxoEHHliKIwAAyDzz589Puxv1/fffj4KCgmjcuHFERPzjH/+I9evXx0MPPZR213RJP7FT+JTXLed07733XkREasyIiG7dukW9evVi4sSJcdhhh8XatWtLfSd0YdxbKs3cs/Au763nzIV31RYqvJO2YsWKO5Ub5OXlxeOPPx6HH354qW7s6t27d1xyySXx6aefxj333BPdu3dP+3mivLy8SJIkmjRpkrrje1t2Jgdr3rx53H///UXaS5s75OXlxQsvvBAbN24s8UlZ28tlPvjggyLtW1+b+++/P3JycmLGjBmRnZ2dah83blyJY5fG1p+niK8/t5UrVy7xjvhCpb0+hfnp/Pnz0/LXjRs3xsKFC+MHP/hBkW0WLlwY5cqVK9V1BwDg+2lH84GtNW/ePCK+nltumS+Vdm6dl5cXBQUFMW/evBILSyNKnu9vmYfVrFkz1b71XP+pp56KFStWxAMPPJC2/rX1msuO+uSTT+LLL79Me1ptcTlqcQp/Ma969erbzA3r1asXubm5xeYVJa1dFB6XtQsAAEqyI+szVapUiWOPPTamTJkSI0aMiEmTJkW7du1ir732SutXvnz5OOOMM2L8+PFxww03xLRp0+Lcc88tscB3a6VdA9pacfVMEV/nBVs+3fSb1qlZ67DW8X20+x5H9B3Url27eOGFF2LmzJmpotq6devGgQceGDfccEOqz65SpUqVYn86p1evXvHxxx/H7bffXuS9r776Kr788ssSx1yzZk1s2rQpra1169ZRrly5nbprtziHHHJI1K9fP0aPHp025sMPPxxvv/12dO/evcg2lSpVigceeCB+/OMfx3HHHRf//ve/I+LrfzhOOumkuP/++4stbF22bNl242nTpk289NJLaW2F//BsfSfC1o9YL1euXPTs2TP+8Y9/FBljy+0Lv/gp7h+bvLy8eOaZZ9Laxo4dW+SOh/LlyxeJZ8qUKfHxxx+XcGSlc9ddd8Xnn3+eej116tT49NNP4+ijj97utqX9rBWO9ac//Smtz9bnc0uvvPJKtG3btjSHAACQcf7yl7+kvb711lsj4v/mXcXNZ1evXl1iYv/JJ5/E3//+99TrNWvWxF133RUHH3xw2s/KVKhQIU4//fSYPHlyjB8/Plq3bl2qm7EK7czcs1GjRlG+fPkic+ZRo0alva5fv3507NgxxowZU+QGtYjt5wa9evWKzZs3x7Bhw4q8t2nTpiJz+dNPPz2ysrLiwgsvjA8++CDOPPPMtPdPPPHEKF++fAwdOrTIPD5JklixYkVEfLMcrE2bNrFy5coiX/aUNnc46aSTYvny5fHnP/+5yNiF21euXDkiSs5l3nnnnbRzO3fu3Jg9e3aReLKystJynEWLFsW0adO2eXzb89xzz8Urr7ySev3hhx/Ggw8+GF26dNnul4mlvT6HHHJI1KtXL0aPHh0bNmxI9Rk/fnyx5yQi4uWXX46WLVumfsILAIDMU9r5ZknatGkTEVHs2kVp5tY9e/aMcuXKxTXXXFPkCU5bxlOlSpUS5/oRkZaHffnll3HnnXcWiWfrMTds2FAkX9tRmzZtijFjxqSNOWbMmKhXr17k5+dvc9v8/PzIy8uLP/zhD/HFF18Ueb8wfylfvnx07do1pk2bFv/5z39S77/99tsxY8aMYsd++eWXIysrK3V9AABgazu6PnPqqafGJ598En/9619j7ty5ceqppxbb7+c//3msXLkyBgwYEF988UWRNYltKe0a0Nby8vLi+eefT/tufPr06fHhhx+m9fsmdWoR1jqsdXw/eVLtFtq1axfXXXddfPjhh2nFs+3bt48xY8ZE48aNt/nTnTsqPz8/Jk2aFJdcckn8+Mc/jqpVq8Zxxx0XP//5z2Py5Mlx3nnnxcyZM+Pwww+PzZs3xzvvvBOTJ0+OGTNmxCGHHFLsmE8++WRccMEFccopp8T+++8fmzZtirvvvjtVvLorVKxYMW644YY466yzokOHDnH66afHkiVLYuTIkdG4ceO4+OKLi90uNzc3pk+fHp06dYqjjz46nn766WjVqlX8/ve/j5kzZ8Zhhx0W5557brRo0SI+++yzeOWVV+Lxxx+Pzz77bJvxHH/88XH33XfHe++9l6qur169erRv3z5uvPHG2LhxYzRs2DAeffTRYu+uvv766+PRRx+NDh06RP/+/ePAAw+MTz/9NKZMmRLPPvts1KxZMw4++OAoX7583HDDDbF69erIzs6OTp06Rf369eOcc86J8847L0466aT46U9/GnPnzo0ZM2YUefrsscceG9dcc02cddZZ0bZt23jjjTdi4sSJaXd/lGTRokXRpEmT6NOnT4wfPz7tvdq1a8cRRxwRZ511VixZsiRuueWWaNasWZx77rnbHbe0n7WDDz44Tj/99Bg1alSsXr062rZtG0888US8//77xY778ssvx2effRbHH3/8dmMAAMhECxcujB49ekS3bt3iueeeiwkTJsQZZ5yRupO0S5cuUalSpTjuuONSX7DcfvvtUb9+/WILTvfff//o169fvPjii7HHHnvEHXfcEUuWLCn2S57evXvHn/70p5g5c2bq5sHS2pm5Z40aNeKUU06JW2+9NbKysiIvLy+mT58eS5cuLdL3L3/5SxxxxBHRunXrOPfcc6Np06axZMmSeO655+Kjjz6KuXPnlrifDh06xIABA2L48OHx2muvRZcuXaJixYoxf/78mDJlSowcOTJOPvnkVP969epFt27dYsqUKVGzZs0iNwfm5eXFtddeG1dccUUsWrQoevbsGdWqVYuFCxfG3//+9+jfv39ceuml3ygH6969e1SoUCEef/zx6N+/f6q9tLlD796946677opLLrkk/v3vf0e7du3iyy+/jMcffzwGDhwYxx9/fOTm5kaLFi1i0qRJsf/++0ft2rWjVatW0apVqzj77LNjxIgR0bVr1+jXr18sXbo0Ro8eHS1btow1a9akxTlixIjo1q1bnHHGGbF06dL4y1/+Es2aNYvXX399m8cY8X93yi9atCitvVWrVtG1a9cYNGhQZGdnpxbuhw4dut0xS3t9KlasGNdee20MGDAgOnXqFKeeemosXLgwxo0bV2wutnHjxnj66adj4MCB240BAIDvr9LON0vStGnTaNWqVTz++ONx9tlnp9pLO7du1qxZ/Pa3v41hw4ZFu3bt4sQTT4zs7Ox48cUXY6+99orhw4dHxNfrTLfddltce+210axZs6hfv3506tQpunTpEvvuu2/069cvfv3rX0f58uXjjjvuiHr16qUVoLZt2zZq1aoVffr0iUGDBkVWVlbcfffdxf50anH69u0bd955ZyxcuDDtCVl77bVX3HDDDbFo0aLYf//9Y9KkSfHaa6/F2LFjS3zyVKFy5crFX//61zj66KOjZcuWcdZZZ0XDhg3j448/jpkzZ0b16tXjH//4R0R8nTs88sgj0a5duxg4cGBs2rQpbr311mjZsmWxucpjjz0Whx9+eNSpU6dUxwcAQObZ0fWZY445JqpVqxaXXnrpNtcFfvjDH0arVq1iypQpceCBB8aPfvSjUse0I2tAWzrnnHNi6tSp0a1bt+jVq1csWLAgJkyYkLoJr9A3qVOLsNZhreN7KiFlzZo1Sfny5ZNq1aolmzZtSrVPmDAhiYjk5z//eZFtGjVqlHTv3r1Ie4cOHZIOHTqkXs+cOTOJiGTmzJmpti+++CI544wzkpo1ayYRkTRq1Cj13oYNG5IbbrghadmyZZKdnZ3UqlUryc/PT4YOHZqsXr26xGP44IMPkrPPPjvJy8tLcnJyktq1aydHHnlk8vjjj6f1i4jk/PPPT2tbuHBhEhHJTTfdlNZeGPuUKVPS2idNmpT88Ic/TLKzs5PatWsnP/vZz5KPPvoorU+fPn2SKlWqpLUtX748adGiRdKgQYNk/vz5SZIkyZIlS5Lzzz8/2WeffZKKFSsmDRo0SDp37pyMHTu2xGMttH79+qRu3brJsGHD0to/+uij5IQTTkhq1qyZ1KhRIznllFOSTz75JImIZPDgwWl9Fy9enPTu3TupV69ekp2dnTRt2jQ5//zzk/Xr16f63H777UnTpk2T8uXLp13LzZs3J5dffnlSt27dpHLlyknXrl2T999/P2nUqFHSp0+f1Pbr1q1LfvWrXyV77rlnkpubmxx++OHJc889V+SzUngdxo0bl2p74403kohIfvOb36TaCq/Lvffem1xxxRVJ/fr1k9zc3KR79+7J4sWL046vQ4cOScuWLYs9f6X9rH311VfJoEGDkjp16iRVqlRJjjvuuOTDDz8s9nxefvnlyb777psUFBQUu08AgEw1ePDgJCKSefPmJSeffHJSrVq1pFatWskFF1yQfPXVV2l9H3rooeSggw5KcnJyksaNGyc33HBDcscddyQRkSxcuDDVrzAnmTFjRnLQQQcl2dnZSfPmzYvM37fUsmXLpFy5ckXm7yXZkblnnz590nKbJEmSZcuWJSeddFJSuXLlpFatWsmAAQOSN998s8i8N0mSZMGCBUnv3r2TBg0aJBUrVkwaNmyYHHvsscnUqVNLFevYsWOT/Pz8JDc3N6lWrVrSunXr5LLLLks++eSTIn0nT56cRETSv3//Ese7//77kyOOOCKpUqVKUqVKlaR58+bJ+eefn7z77rtJkpQ+BytJjx49ks6dO6e1lTZ3SJIkWbt2bfLb3/42adKkSSqXOvnkk5MFCxak+syZMyfJz89PKlWqVGT+PmHChKRp06ZJpUqVkoMPPjiZMWNGsdfwb3/7W7LffvulPl/jxo1LfZ63tHUelCRJUrdu3eQnP/lJWlthTjphwoTUuD/84Q/TcuYk+b8/M8uWLSv2/G3v+hQaNWpU0qRJkyQ7Ozs55JBDkmeeeabY8/nwww8nEZHKVQEA+PYZN25ckbyopDWZrfOiwm1ffPHFtPaS5p2lnW8WZ8SIEUnVqlWTtWvXprWXdm6dJElyxx13pNZhatWqlXTo0CF57LHHUu//97//Tbp3755Uq1YtiYi0c/Dyyy8nhx12WFKpUqVk3333TUaMGFHsuZs9e3byk5/8JMnNzU322muv5LLLLktmzJhRZE2ruDzhpJNOSnJzc5OVK1em2grXI1566aWkTZs2SU5OTtKoUaPkz3/+c9q2JV2jQq+++mpy4oknJnXq1Emys7OTRo0aJb169UqeeOKJtH5PP/10Kt9p2rRpMnr06GLP56pVq5JKlSolf/3rX4vdHwAA328lzfmLmyOXdn2m0M9+9rMkIpKjjjpqmzHceOONSUQk119/fanjLu0aUHF1aUmSJDfffHPSsGHDJDs7Ozn88MOTl156qdjvxne2Tq2QtQ5rHd83WUlSyttN4Vts2LBhMW7cuJg/f/52H539XTRq1Ki47LLLYsGCBbHHHntERMRTTz0VRx55ZEyZMiXtiVtlbf369dG4ceP4zW9+ExdeeGFZhwMAQDF++MMfRu3ateOJJ54o61DK1IMPPhg9e/aMZ555Ju3XSv6XZs2aFR07dox33nkn9ttvvzKJYXeaN29etGzZMqZPn572NOCsrKw4//zzi/05p7LUs2fPyMrKSvspLQAA2BmrV6+Opk2bxo0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",
      "text/plain": [
       "<Figure size 4000x2000 with 32 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_compare_example(name_str,color,pred_color):\n",
    "    plt.axis(\"off\")\n",
    "    plt.title(name_str)\n",
    "    plt.imshow(np.concatenate([color.reshape([1,1,3]),pred_color.reshape([1,1,3])],axis=1))\n",
    "\n",
    "\n",
    "for data in train_ds.take(1):\n",
    "    tokens=data[0].numpy()\n",
    "    colors=data[1].numpy()\n",
    "    y_preds=model.predict(tokens,verbose=0)\n",
    "    \n",
    "    cols=4\n",
    "    rows=math.ceil(len(tokens)/cols)\n",
    "    size=5\n",
    "\n",
    "    fig=plt.figure(figsize=(size*rows,size*cols))\n",
    "\n",
    "    for idx in range(len(tokens)):\n",
    "        name_str=one_hot_tokens_to_str(tokens[idx])\n",
    "        color=(colors[idx]*255).astype(\"uint8\")\n",
    "        pred_color=(y_preds[idx]*255).astype(\"uint8\")\n",
    "        \n",
    "        fig.add_subplot(rows,cols,idx+1)    \n",
    "        plot_compare_example(name_str+\" (actual,pred)\",color,pred_color)\n",
    "    \n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "9d613e0b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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fPn3KXP+gQYNStWrV1rkGAIBcsaXUOcXnfSeddFJW+wUXXJAiIj333HPl2v+GrmHN8/M5c+akgoKCrHP8s846K+Xl5aU333wz0zZ//vxUv379FBFpxowZKaWUvvrqq1S3bt108sknZ+3nf//7X6pTp06J9rXddtttKSLSO++8s1FrWrRoUapVq1bac889s2qqlFJWLdezZ8/UokWLEuONGTMmaz3Fis/916xHSpvPNddck/Ly8rJeZ6XVbqXp169fioh01llnZc25Z8+eqWrVqmnu3Lkppf+rV2rXrp3mzJmTNUa3bt3SzjvvnJYvX541RqdOndJ2222XaTvnnHNSRKTXXnst0zZnzpxUp06dUtefUkpVq1ZNp5122nrXAQDAhsuleuWKK65IEZEeeuihEuMXn7MXn3/vsMMOacWKFZnHR40aVaJe6NevX9a5/YQJE1JEpGuvvTbTtmrVqtS5c+dyve+vJlGTAACw4baUmqZ42/z8/PTee+9ltZc3OzVy5MgUEWncuHGZPl9//XVq27ZtmbmmNV1++eUpItJXX31V4rG1j83KlSvTTjvtlPbff/9M28cff5zy8/PTkUceWSIDtWbN0b59+9SlS5cS+yirPiitFintuTrllFNS9erVs875167LylKcnfp//+//ZdpWrFiRdtlll9S4ceO0cuXKlNL/1T+tW7fOmsOG5Pc25DWZUkqff/55iog0YsSI9a6D8sv/jpncH4zVq1fHU089FUcccUQ0b948077DDjtk7mq6pjU/Mbp48eKYN29edOnSJT799NNYvHhxVt9WrVqVOkbEt58cPvPMM2PUqFFxzz33lOsTw8W33j799NOz2s8666wytzn11FOzfn7ooYeiqKgoevfuHfPmzcv8r0mTJrHddtvF5MmTIyJi6tSp8fHHH0efPn1i/vz5mX5ff/11dOvWLf7+97+X+EqatffVuXPnmD9/fuZW1KV5/fXXY86cOXH66adHYWFhpr1nz57Rrl27eOKJJ9ZxREo69thj45tvvomHHnoo0/b000/HokWL4thjj42Ib4/9gw8+GIcddliklLKOQ48ePWLx4sXxxhtvlLmPBQsWREop6tWrV+KxNV8fy5cvj3nz5kXHjh0jIjJjFhUVxYQJE+Kwww6L3XbbrcQYpd1yfGMVFBREfv63v66rV6+O+fPnR82aNWP77bdf5xrXp2/fvlGrVq3Mz0cffXRsvfXW8be//a3E/gcMGJDVNn78+Nhhhx2iXbt2Wcd+//33j4jIvAaLxzr77LOztl/XXYTr1asXy5Yti6VLl2702gAAfgy2pDqn+LzvvPPOy2o///zzIyJK1ARl7X9D1rDjjjtG586dMz83atQott9++/j0008zbRMnToy99tordtlll0xb/fr144QTTsgaa9KkSbFo0aI4/vjjs85vK1WqFHvuuWfm/LYs8+fPj4hYb31R1pomTZoUX331VVx88cVZNVXEpq0t1p7P119/HfPmzYtOnTpFSinefPPNjR73zDPPzPx38SfiV65cGc8880xWv6OOOioaNWqU+XnBggXx3HPPRe/eveOrr77KHPv58+dHjx494uOPP47Zs2dHxLevs44dO2Z940qjRo1KPJ9rqlevXomv4wUA4LvLtXrlwQcfjJ/97GeZO5auae1z9gEDBkTVqlUzPxfXLWvWKqXNsXLlynHaaadl2ipVqrTOa0drUpOoSQAA2DBbUk1TrEuXLrHjjjtmjVXe7NTf/va32HrrrePoo4/ObF+9evUYNGhQufY9f/78qFy5cqnfVrHmsVm4cGEsXrw4OnfunJVpmjBhQhQVFcUVV1yRyUAV25w1R/E5fufOnWPp0qXxwQcfbNSYlStXjlNOOSXzc9WqVeOUU06JOXPmxJQpU7L69uvXL2sO5c3vbehrMuL/akA1x6ZVuaInsKnMnTs3li1bFtttt12Jx7bffvsSIcGXX345Bg8eHK+++mqJ4N7ixYujTp06mZ9btWpV5n7vuuuuWLJkSdx8881x/PHHl2uus2bNivz8/BLjtm3btsxt1u778ccfR0qp1PVGRFSpUiXTLyLW+Qd48eLFWW+yrPlLGfF/v3wLFy6M2rVrlzrGrFmzIiJKvV15u3bt4qWXXipz/6X52c9+Fu3atYv7778/TjzxxIiIuP/++6Nhw4aZ0ObcuXNj0aJFMXr06Bg9enSp48yZM2e9+0oplWhbsGBBDB06NO67774SYxT/Qzh37tz48ssvY6eddtqgtW2MoqKiGDVqVNx0000xY8aMWL16deaxBg0abPS4a79+8vLyom3btjFz5sys9mbNmmW9ARjx7Wvr/fffz3rjaU3Fx6349d6mTZusx8u6tX3E/z0nm/ofTQCALc2WWOesXdc0adIk6tatm6kZ1rf/DVnD2rVLxLf1y8KFC7Pmtddee5Xot/Y8i2un4npjbWXVQmsrrb4oz5qmT58eEfG91Bf//ve/44orrohHH30061gVz2dj5OfnR+vWrbPafvKTn0RElKgv1n7uP/nkk0gpxW9/+9v47W9/W+r4c+bMiWbNmsWsWbNizz33LPH4+uoLtQUAwKaXa/XK9OnT46ijjirX/tZ1nWVdc9x6661LXKBe17luadQk/0dNAgDAumxJNU1Z425IdmrWrFnRtm3bEuemG1pzlObxxx+PK6+8MqZOnRorVqzItK+5r+nTp0d+fn5WKHhzee+99+Lyyy+P5557rsRNJDe25mjatGnUqFEjq23NmqP4Zo0Rpef8Itaf31uxYsUGvSYjZKw2lx9NqHZDTJ8+Pbp16xbt2rWL66+/PrbddtuoWrVq/O1vf4s//OEPJe7cumZyfG177713TJ06NW688cbo3bt31K9ff7PMee05FBUVRV5eXjz55JNRqVKlEv2L33QpXst1112XdWem0voWK228iNLfiNmcjj322Ljqqqti3rx5UatWrXj00Ufj+OOPj8qVv33ZFq/tl7/8ZZl/dH7605+WOX79+vUjLy+v1DexevfuHa+88kr85je/iV122SVq1qwZRUVFcdBBB5V4fWwOa4ZmIyKuvvrq+O1vfxsDBw6M4cOHR/369SM/Pz/OOeec72U+pf0OFBUVxc477xzXX399qdtsu+22G72/hQsXRvXq1df5uwcAQLYfSp1T3qK9tP1v6Bo2Ze1SPPbdd98dTZo0KfF4cR1SluIPuy1cuDC22WabTPuGrmljlXXc164tVq9eHQceeGAsWLAgLrroomjXrl3UqFEjZs+eHf3796+Q+qJ4nxdccEGZn7Re14dQ12fRokXRsGHDjd4eAIDv7sdQr2yIirjOoiYpPzUJAAAb6odS05R1Lrux2akN0aBBg1i1alV89dVXWd+K/eKLL0avXr1i3333jZtuuim23nrrqFKlSowZMyb+8pe/bJJ9R5S/5li0aFF06dIlateuHcOGDYs2bdpEYWFhvPHGG3HRRRdVaM2xvvzemoHk8irOvak5Nq0fTai2UaNGUa1atUyye00ffvhh1s+PPfZYrFixIh599NGsTwuv7+s8S9O2bdu49tpro2vXrnHQQQfFs88+m/WHozQtWrSIoqKimDFjRlay/JNPPin3ftu0aRMppWjVqlUm9V5Wv4hv76p0wAEHlHv8DdWiRYuI+PZYr31npw8//DDz+IY49thjY+jQofHggw/GVlttFV9++WUcd9xxmccbNWoUtWrVitWrV2/U2ipXrhxt2rSJGTNmZLUvXLgwnn322Rg6dGhcccUVmfa1X1uNGjWK2rVrx7vvvrvO/azrTbp69erFokWLstpWrlwZ//3vf7PaHnjggdhvv/3i9ttvz2r/rm/ErL2mlFJ88skn5foHtU2bNvHWW29Ft27d1rnG4tf79OnTsz7dsvbv5ZpmzJgRO+ywQzlWAADw47Yl1jkff/xx1rncF198EYsWLSpXTbAp17DmvEqrtdZuK66dGjduvFH1Rbt27SLi23PZnXfeOdNe3jUV7//dd99d58Xass69i+98tXZ9sfYdt95555346KOP4s4774y+fftm2idNmlTmPsujqKgoPv3006z69KOPPoqIiJYtW65z2+K7SVWpUmW9x75Fixbl+n0oNnv27Fi5cqX6AgBgM8i1eqVNmzbrvR7wXbRo0SKeffbZWLJkSdbNUNb1Xvqa1CRqEgAANsyWVNOUZUOyUy1atIh33323xDcpbEzNsWau6MEHH4zCwsJ46qmnoqCgINM+ZsyYrO3btGkTRUVFMW3atDKDpRHlqznq1q2baV+75nj++edj/vz58dBDD8W+++6baV87H7ahPv/88/j666+z7lZb3pqjvPm9DXlNFitel5pj08qv6AlsKpUqVYoePXrEhAkT4t///nem/f3334+nnnqqRN+I7E8EL168uMQvc3n99Kc/jb/97W/x/vvvx2GHHRbLli1bZ//iT7nedNNNWe033HBDuff5i1/8IipVqhRDhw4t8cnmlFLMnz8/IiI6dOgQbdq0id///vexZMmSEuPMnTu33Ptcl9122y0aN24ct9xyS1Zq/sknn4z3338/evbsucFj7rDDDrHzzjvH/fffH/fff39svfXWWX/sKlWqFEcddVQ8+OCDpb6RVZ617bXXXvH6669ntZX2+oiIGDlyZNbP+fn5ccQRR8Rjjz1WYow1ty/+Y7r2G0kR3/7R/Pvf/57VNnr06BKfoqhUqVKJ+YwfPz5mz55dxsrK56677oqvvvoq8/MDDzwQ//3vf+Pggw9e77a9e/eO2bNnx5///OcSjy1btiy+/vrriIjMWH/84x+z+qx9PNf0xhtvRKdOncqzBACAH7Utqc455JBDIqLkeV7xNxuUpybY1GuI+Lb+evXVV2Pq1KmZtgULFsS9995bol/t2rXj6quvjm+++abEOOurLzp06BBVq1YtV31R2pq6d+8etWrVimuuuSaWL1+e9dia29aoUaPUryYqfkNmzfpi9erVJb7uqbT5pJRi1KhR61xfedx4441ZY954441RpUqV6Nat2zq3a9y4cXTt2jVuvfXWEh8wjMg+9occckj84x//iH/+859Zj6/9fBabMmVKRIT6AgBgM8i1euWoo46Kt956Kx5++OESj22KO9AecsghsWrVqrj55pszbatXry73tSM1iZoEAIANsyXVNGXZkOzUIYccEp9//nk88MADmbalS5eWOGcvy1577RURUWrNkZeXl5V1mjlzZkyYMCGr3xFHHBH5+fkxbNiwEneLXbvmKCtjFZFdc3z99ddx5513lpjP2mOuXLmyRE5vQ61atSpuvfXWrDFvvfXWaNSoUXTo0GGd25Y3v7chr8liU6ZMiby8vMzzw6bxo7lTbUTE0KFDY+LEidG5c+c4/fTTY9WqVXHDDTdE+/bt4+2338706969e1StWjUOO+ywOOWUU2LJkiXx5z//ORo3blxqsVweHTt2jEceeSQOOeSQOProo2PChAlRpUqVUvt26NAhjjrqqBg5cmTMnz8/OnbsGC+88EImvV6erx9q06ZNXHnllXHJJZfEzJkz44gjjohatWrFjBkz4uGHH45BgwbFBRdcEPn5+XHbbbfFwQcfHO3bt48BAwZEs2bNYvbs2TF58uSoXbt2PPbYYxu15jVVqVIlRowYEQMGDIguXbrE8ccfH1988UWMGjUqWrZsGeeee+5GjXvsscfGFVdcEYWFhXHiiSdGfn52Dvx3v/tdTJ48Ofbcc884+eSTY8cdd4wFCxbEG2+8Ec8880wsWLBgneMffvjhcffdd8dHH32U+fRy7dq1Y999941rr702vvnmm2jWrFk8/fTTpX5i4eqrr46nn346unTpEoMGDYoddtgh/vvf/8b48ePjpZdeirp168Yuu+wSlSpVihEjRsTixYujoKAg9t9//2jcuHGcdNJJceqpp8ZRRx0VBx54YLz11lvx1FNPlbj77KGHHhrDhg2LAQMGRKdOneKdd96Je++9N/Pp6XWZOXNmtGrVKvr16xdjx47Neqx+/fqxzz77xIABA+KLL76IkSNHRtu2bePkk09e77i/+tWvYty4cXHqqafG5MmTY++9947Vq1fHBx98EOPGjYunnnoqdtttt9hll13i+OOPj5tuuikWL14cnTp1imeffbbMOzNPmTIlFixYEIcffvh65wAAkAu2lDrnZz/7WfTr1y9Gjx6d+Wqdf/7zn3HnnXfGEUccEfvtt99697c51nDhhRfGPffcEwceeGCcddZZUaNGjbjtttuiefPmsWDBgkz9Vbt27bj55pvjV7/6Vfz85z+P4447Lho1ahT//ve/44knnoi999476wLt2goLC6N79+7xzDPPxLBhwzZ4TbVr144//OEPcdJJJ8Xuu+8effr0iXr16sVbb70VS5cuzbwp1KFDh7j//vvjvPPOi9133z1q1qwZhx12WLRv3z46duwYl1xySSxYsCDq168f9913X6xatSprnu3atYs2bdrEBRdcELNnz47atWvHgw8+mPl6oPUZMmRIDB06NCZPnhxdu3bNWv/EiROjX79+seeee8aTTz4ZTzzxRFx66aXRqFGj9Y77pz/9KfbZZ5/Yeeed4+STT47WrVvHF198Ea+++mp89tln8dZbb0XEt8/n3XffHQcddFD8+te/jho1asTo0aOjRYsWWb8PxSZNmhTNmzePXXfdtVzrAwBgw+RSvfKb3/wmHnjggTjmmGNi4MCB0aFDh1iwYEE8+uijccstt8TPfvazjVpHscMOOyz23nvvuPjii2PmzJmx4447xkMPPVRqgLU0ahI1CQAAG25LqWnWpbzZqZNPPjluvPHG6Nu3b0yZMiW23nrruPvuu6N69erl2k/r1q1jp512imeeeSYGDhyYae/Zs2dcf/31cdBBB0WfPn1izpw58ac//Snatm2bdQzbtm0bl112WQwfPjw6d+4cv/jFL6KgoCD+9a9/RdOmTeOaa66JiG9rjptvvjmuvPLKaNu2bTRu3Dj233//6N69ezRv3jxOPPHE+M1vfhOVKlWKO+64I3Mtp1inTp2iXr160a9fvzj77LMjLy8v7r777nJ/GLJ///5x5513xowZM7LuQNu0adMYMWJEzJw5M37yk5/E/fffH1OnTo3Ro0ev93nbkPxeeV+TxSZNmhR77713NGjQoFzro5zSj8wLL7yQOnTokKpWrZpat26dbrnlljR48OC09lIfffTR9NOf/jQVFhamli1bphEjRqQ77rgjRUSaMWNGpl+LFi1Sz549S91XRKQzzjgjq+2RRx5JlStXTscee2xavXp1mfP8+uuv0xlnnJHq16+fatasmY444oj04YcfpohIv/vd7zL9iuc+d+7cUsd58MEH0z777JNq1KiRatSokdq1a5fOOOOM9OGHH2b1e/PNN9MvfvGL1KBBg1RQUJBatGiRevfunZ599tn17mvMmDEljktZ7r///rTrrrumgoKCVL9+/XTCCSekzz77LKtPac9HixYtUr9+/UqM9/HHH6eISBGRXnrppVL3+cUXX6QzzjgjbbvttqlKlSqpSZMmqVu3bmn06NHrne+KFStSw4YN0/Dhw7PaP/vss3TkkUemunXrpjp16qRjjjkmff755yki0uDBg7P6zpo1K/Xt2zc1atQoFRQUpNatW6czzjgjrVixItPnz3/+c2rdunWqVKlSiog0efLklFJKq1evThdddFFq2LBhql69eurRo0f65JNPShyP5cuXp/PPPz9tvfXWqVq1amnvvfdOr776aurSpUvq0qVLpt+MGTNSRKQxY8Zk2t55550UEeniiy/OtE2ePDlFRPrrX/+aLrnkktS4ceNUrVq11LNnzzRr1qys9XXp0iW1b9++1OO3cuXKNGLEiNS+fftUUFCQ6tWrlzp06JCGDh2aFi9enOm3bNmydPbZZ6cGDRqkGjVqpMMOOyz95z//KfV4XnTRRal58+apqKio1H0CAOSiLaXO+eabb9LQoUNTq1atUpUqVdK2226bLrnkkrR8+fKsfuva/3ddw9rnyCl9Ww917tw5FRQUpG222SZdc8016Y9//GOKiPS///0vq+/kyZNTjx49Up06dVJhYWFq06ZN6t+/f3r99dfLXHexhx56KOXl5aV///vfG7Wm4r6dOnVK1apVS7Vr10577LFH+utf/5p5fMmSJalPnz6pbt26KSJSixYtMo9Nnz49HXDAAamgoCBttdVW6dJLL02TJk3KqkFSSmnatGnpgAMOSDVr1kwNGzZMJ598cnrrrbdK1BKlvcbOP//8lJeXl95///1MW79+/VKNGjXS9OnTU/fu3VP16tXTVlttlQYPHpz1eimuV6677rpSj9/06dNT3759U5MmTVKVKlVSs2bN0qGHHpoeeOCBrH5vv/126tKlSyosLEzNmjVLw4cPT7fffnuJ47l69eq09dZbp8svv7zU/QEAsGnkUr0yf/78dOaZZ6ZmzZqlqlWrpm222Sb169cvzZs3L6X0f++9jx8/Pmu70t6779evX9b5fPH4v/rVr1Lt2rVTnTp10q9+9av05ptvlti2LGoSNQkAABtuS6lpStu2WHmzU7NmzUq9evVK1atXTw0bNky//vWv08SJE0ucs5fl+uuvTzVr1kxLly7Nar/99tvTdtttlwoKClK7du3SmDFjSj2GKaV0xx13ZLJl9erVS126dEmTJk3KPP6///0v9ezZM9WqVStFRNY1nylTpqQ999wzVa1aNTVv3jxdf/31pebaXn755dSxY8dUrVq11LRp03ThhRemp556qsQ6S6vLjjrqqFStWrW0cOHCTFtxdur1119Pe+21VyosLEwtWrRIN954Y9a2ZdWExcqT30up/K/JRYsWpapVq6bbbrut1P2x8fJS2gTfScMmMXXq1Nh1113jnnvuiRNOOKGip5Mzhg8fHmPGjImPP/44cwvwH5ObbropLrzwwpg+fXpstdVWERHx/PPPx3777Rfjx4+Po48+uoJn+H9WrFgRLVu2jIsvvjh+/etfV/R0AAD4ETvnnHPi1ltvjSVLlmyyOmD16tWx4447Ru/evWP48OGbZMwfmj322CNatGgR48ePz7T1798/HnjggVK/sqgiTZgwIfr06RPTp0+PrbfeuqKnAwAAm52aRE0CAACb0+LFi6N169Zx7bXXxoknnljR09ksttpqq+jbt29cd911mbauXbvGvHnz4t13363AmZU0cuTIuPbaa2P69OlRrVq1ip7Oj0p+RU8gVy1btqxE28iRIyM/Pz/23XffCphR7jr33HNjyZIlcd9991X0VDaLyZMnx9lnn50J1P6QjRkzJqpUqRKnnnpqRU8FAIAfkbXrr/nz58fdd98d++yzzyb9YF2lSpVi2LBh8ac//ekHdzF3U/jyyy/jrbfeyvoq2R+yESNGxJlnnuniNQAAOUNN8sOiJgEA4MemTp06ceGFF8Z1110XRUVFFT2dTe69996LZcuWxUUXXVTRU1mvb775Jq6//vq4/PLLBWo3A3eqrSBDhw6NKVOmxH777ReVK1eOJ598Mp588skYNGhQ3HrrrRU9PX7kfqh3qgUAgM1hl112ia5du8YOO+wQX3zxRdx+++3x+eefx7PPPutDjZvAD/WuUAAAQG5QkwAAAJvTD/VOtWw+lSt6ArmqU6dOMWnSpBg+fHgsWbIkmjdvHkOGDInLLrusoqcGAADwo3LIIYfEAw88EKNHj468vLz4+c9/HrfffrtALQAAAAAAAJDFnWoBAAAAAAAAAAAAyHn5FT0BAAAAAAAAAAAAAKhoQrUAAAAAAAAAAAAA5DyhWgAAAAAAAAAAAAByXuXydux95i6bcRqwYd58b/uKngJkfDz5/oqeAgDkpv+XV9EzgIz5/61b0VOAjAa/X1jRUwCAnDO6w5SKngJkNKlTtaKnABm9ntu5oqcAADnp93muofDDUaWGGoUfjl8vWbHePu5UCwAAAAAAAAAAAEDOE6oFAAAAAAAAAAAAIOcJ1QIAAAAAAAAAAAC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      "text/plain": [
       "<Figure size 4000x2000 with 32 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for data in val_ds.take(1):\n",
    "    tokens=data[0].numpy()\n",
    "    colors=data[1].numpy()\n",
    "    y_preds=model.predict(tokens,verbose=0)\n",
    "    \n",
    "    cols=4\n",
    "    rows=math.ceil(len(tokens)/cols)\n",
    "    size=5\n",
    "\n",
    "    fig=plt.figure(figsize=(size*rows,size*cols))\n",
    "\n",
    "    for idx in range(len(tokens)):\n",
    "        name_str=one_hot_tokens_to_str(tokens[idx])\n",
    "        color=(colors[idx]*255).astype(\"uint8\")\n",
    "        pred_color=(y_preds[idx]*255).astype(\"uint8\")\n",
    "        \n",
    "        fig.add_subplot(rows,cols,idx+1)    \n",
    "        plot_compare_example(name_str+\" (actual,pred)\",color,pred_color)\n",
    "    \n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "09d48601",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def test_predict_show(names):\n",
    "    test_data=preprocess(names)\n",
    "    colors=(model.predict(test_data,verbose=0) * 255.0).astype(\"uint8\")\n",
    "    \n",
    "    for idx in range(len(names)):\n",
    "        plot_single_example(names[idx],colors[idx])\n",
    "        plt.show()\n",
    "\n",
    "test_predict_show([\"red\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "efce0745",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_predict_show([\"light purple\",\"purple\",\"dark purple\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "c75af18f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_predict_show([\"light violet\",\"violet\",\"dark violet\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "af8b9d3e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAGbCAYAAAAr/4yjAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8pXeV/AAAACXBIWXMAAA9hAAAPYQGoP6dpAAAKLElEQVR4nO3cT2hddRrH4femxdRCK1JjJ0IcbKKYptaFAaldKBrQIoWCIC4URalUKehC3ImlC9GNBf/WCi4UESrV4MKoFCKoGxetQUsLCUKFSGhD/QMas7g9sxj6ZTLtqKNT09t5HgjknPuenN+5BD6cnCStpmmaAoCq6lrsBQBw7hAFAEIUAAhRACBEAYAQBQBCFAAIUQAgRAGAEAUAQhQACFEAIESB/xsnT56sX375ZbGXAec0UaAjffzxxzU8PFzLli2r/v7+euWVV2rHjh3VarUy02q1avv27fXmm2/W0NBQdXd31wcffFBVVdPT03X//ffX6tWrq7u7u4aGhuq111477Tzz8/P15JNP1sDAQHV3d1dfX189/vjjNT8/v2Du1LlGR0dr3bp1+ZqnzgedouVfZ9NpDh48WBs2bKje3t7atm1btdvtevHFF6unp6cmJibq1Ld0q9WqwcHBmp2dre3bt9cll1xSN9xwQ/X29tbw8HC1Wq3aunVr9fT01NjYWL333nu1a9euevTRR6vqn3cWmzZtqk8//bQefPDBGhwcrC+//LJ2795dt99+e42OjmZNrVarrr322jp27Fg9/PDDtWLFinruuedqZmamvvnmm1q1atUivFPwBzTQYTZv3twsX768mZ6ezr7Jyclm6dKlzb9+S1dV09XV1Rw6dGjB8Q888EDT29vbzM7OLth/1113NRdddFHz888/N03TNG+88UbT1dXVfPLJJwvmdu/e3VRV89lnny041wUXXNBMTU1l38TERFNVzfPPP//nLxr+In58REdpt9u1f//+2rJlS1122WXZPzAwUJs2bTpt/sYbb6y1a9dmu2ma2rdvX23evLmapqnZ2dl83HrrrfXDDz/UgQMHqqrq7bffrsHBwbr66qsXzN18881VVTU+Pr7gXCMjI9Xf35/t9evX18qVK+vrr7/+n74HcDYtXewFwH/j2LFjNTc3VwMDA6e9dqZ9V1xxxYLt48eP1/fff1979uypPXv2/MdzVFVNTk7W4cOHq6en51fnTrn88stPm7n44ovru+++O/PFwDlIFDivXXjhhQu2T548WVVVd999d917771nPGb9+vWZveaaa+rZZ58941xfX9+C7SVLlpxxrvHYjg4iCnSUSy+9tJYtW1ZTU1OnvXamff+up6enVqxYUe12u0ZGRn51tr+/vyYmJuqWW25Z8FtNcD7zTIGOsmTJkhoZGanR0dH69ttvs39qaqrGxsZ+1/F33HFH7du3r7766qvTXj9+/Hg+v/POO2t6erpeffXV0+bm5ubqp59++oNXAecudwp0nB07dtRHH31UGzdurIceeqja7Xa98MILtW7duvriiy9+8/inn366xsfH6/rrr6+tW7fW2rVr68SJE3XgwIHav39/nThxoqqq7rnnntq7d29t27atxsfHa+PGjdVut+vIkSO1d+/e+vDDD2t4ePgsXy38tUSBjnPdddfV2NhYPfbYY/XEE09UX19f7dy5sw4fPlxHjhz5zeNXr15dn3/+ee3cubPeeeedeumll2rVqlU1NDRUzzzzTOa6urpqdHS0du3aVa+//nq9++67tXz58lqzZk098sgjddVVV53Ny4RF4Y/XOG9s2bKlDh06VJOTk4u9FOhYninQkebm5hZsT05O1vvvv1833XTT4iwIzhPuFOhIvb29dd9999WaNWvq6NGj9fLLL9f8/HwdPHiwrrzyysVeHnQszxToSLfddlu99dZbNTMzU93d3bVhw4Z66qmnBAH+JHcKAIRnCgCEKAAQv/uZwsqjfzub6wDgLPvx7zO/OeNOAYAQBQBCFAAIUQAgRAGAEAUAQhQACFEAIEQBgBAFAEIUAAhRACBEAYAQBQBCFAAIUQAgRAGAEAUAQhQACFEAIEQBgBAFAEIUAAhRACBEAYAQBQBCFAAIUQAgRAGAEAUAQhQACFEAIEQBgBAFAEIUAAhRACBEAYAQBQBCFAAIUQAgRAGAEAUAQhQACFEAIEQBgBAFAEIUAAhRACBEAYAQBQBCFAAIUQAgRAGAEAUAQhQACFEAIEQBgBAFAEIUAAhRACBEAYAQBQBCFAAIUQAgRAGAEAUAQhQACFEAIEQBgBAFAEIUAAhRACBEAYAQBQBCFAAIUQAgRAGAEAUAQhQACFEAIEQBgBAFAEIUAAhRACBEAYAQBQBCFAAIUQAgRAGAEAUAQhQACFEAIEQBgBAFAEIUAAhRACBEAYAQBQBCFAAIUQAgRAGAEAUAQhQACFEAIEQBgBAFAEIUAAhRACBEAYAQBQBCFAAIUQAgRAGAEAUAQhQACFEAIEQBgBAFAEIUAAhRACBEAYAQBQBCFAAIUQAgRAGAEAUAQhQACFEAIEQBgBAFAEIUAAhRACBEAYAQBQBCFAAIUQAgRAGAEAUAQhQACFEAIEQBgBAFAEIUAAhRACBEAYAQBQBCFAAIUQAgRAGAEAUAQhQACFEAIEQBgBAFAEIUAAhRACBEAYAQBQBCFAAIUQAgRAGAEAUAQhQACFEAIEQBgBAFAEIUAAhRACBEAYAQBQBCFAAIUQAgRAGAEAUAQhQACFEAIEQBgBAFAEIUAAhRACBEAYAQBQBCFAAIUQAgRAGAEAUAQhQACFEAIEQBgBAFAEIUAAhRACBEAYAQBQBCFAAIUQAgRAGAEAUAQhQACFEAIEQBgBAFAEIUAAhRACBEAYAQBQBCFAAIUQAgRAGAEAUAQhQACFEAIEQBgBAFAEIUAAhRACBEAYAQBQBCFAAIUQAgRAGAEAUAQhQACFEAIEQBgBAFAEIUAAhRACBEAYAQBQBCFAAIUQAgRAGAEAUAQhQACFEAIEQBgBAFAEIUAAhRACBEAYAQBQBCFAAIUQAgRAGAEAUAQhQACFEAIEQBgBAFAEIUAAhRACBEAYAQBQBCFAAIUQAgRAGAEAUAQhQACFEAIEQBgBAFAEIUAAhRACBEAYAQBQBCFAAIUQAgRAGAEAUAQhQACFEAIEQBgBAFAEIUAAhRACBEAYAQBQBCFAAIUQAgRAGAEAUAQhQACFEAIEQBgBAFAEIUAAhRACBEAYAQBQBCFAAIUQAgRAGAEAUAQhQACFEAIEQBgBAFAEIUAAhRACBEAYAQBQBCFAAIUQAgRAGAEAUAQhQACFEAIEQBgBAFAEIUAAhRACBEAYAQBQBCFAAIUQAgRAGAEAUAQhQACFEAIEQBgBAFAEIUAAhRACBEAYAQBQBCFAAIUQAgRAGAEAUAQhQACFEAIEQBgBAFAEIUAAhRACBEAYAQBQBCFAAIUQAgRAGAEAUAQhQACFEAIEQBgBAFAEIUAAhRACBEAYAQBQBCFAAIUQAgRAGAEAUAQhQACFEAIEQBgBAFAEIUAAhRACBEAYAQBQBCFAAIUQAgRAGAEAUAQhQACFEAIEQBgGg1TdMs9iIAODe4UwAgRAGAEAUAQhQACFEAIEQBgBAFAEIUAAhRACD+AWydW2uNwNiEAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAGbCAYAAAAr/4yjAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8pXeV/AAAACXBIWXMAAA9hAAAPYQGoP6dpAAANhElEQVR4nO3daYiV9d/H8e+MphmltjJQZmpmy4MW4S6iHNu0hcKKpn2yaOduoXzQRpO00aZ2t1JBC7ZRJK0WkVEQkSBIQVCWFREVNeSUGVHNdT+46XN3mhb1nwv//+sFguc3v3Od7+UD39e5zoy2NU3TFABUVfu6HgCA9YcoABCiAECIAgAhCgCEKAAQogBAiAIAIQoAhCjwH2Hy5Mk1efLkdT0GrPdEAYAQBQBCFFgrvv/++3U9whr3n3CO/PsTBf5xV111VbW1tdW7775bJ5xwQm266aa1zz77VFXVzz//XFdffXWNGzeuhg4dWtttt11ddtll9eOPP+b5p5xySm2xxRb1008/DTj2lClTasKECXl8//331/77719bbbVVDR06tHbeeee66667Vnv2H374oc4///zaYostapNNNqkjjjiiPvvss2pra6urrrpqpc6xqmru3Lk1ceLEGjZsWG222WZ13HHH1aeffjrg9d566606+OCDa8SIEbXRRhtVZ2dnvfHGG3/45/nBBx/U9OnTa+TIkTVixIg69dRTa8WKFat9rvBHRIE15phjjqkVK1bUddddV2eccUZVVZ1++ul15ZVX1h577FGzZ8+uzs7Ouv766+u4447L804++eTq7e2tl156qeV4X3zxRS1YsKBOOumkrN111101evTouuyyy+qWW26pUaNG1bnnnlt33HHHas08ffr0uu222+rQQw+tG264oYYNG1aHHXbYKp3jtddeW93d3TV+/PiaNWtWXXjhhfXKK6/UpEmTatmyZXnuggULatKkSfXtt99WT09PXXfddbVs2bLaf//9a+HChQNeq6urq7777ru6/vrrq6urqx544IGaOXPmap0n/KkG/mE9PT1NVTXHH398y/rixYubqmpOP/30lvUZM2Y0VdUsWLCgaZqm+eWXX5ptttmmOfbYY1v2zZo1q2lra2uWLl2atRUrVgx4/alTpzZjx45tWevs7Gw6Ozv/cu5FixY1VdVceOGFLevTp09vqqrp6en523P8+OOPm0GDBjXXXntty/o777zTDB48OOv9/f3N+PHjm6lTpzb9/f0t5zNmzJjmoIMOGvBap512WssxjzzyyGbzzTf/y3OCVeWdAmvM2Wef3fL4hRdeqKqqiy66qGX94osvrqqq559/vqqq2tvb68QTT6xnnnmmvvvuu+x7+OGHa++9964xY8ZkbdiwYfl9X19fff3119XZ2VlLly6tvr6+VZr3xRdfrKqqc889t2X9vPPO+9Pn/P4cn3rqqerv76+urq76+uuv86ujo6PGjx9fr776alVVLV68uJYsWVInnHBC9fb2Zt/3339fBxxwQL3++uvV39//l6+17777Vm9vb3377berdJ7wVwav6wH49/Xbv7yrqj755JNqb2+v7bffvmW9o6OjRo4cWZ988knWuru764Ybbqh58+ZVd3d3vffee7Vo0aK6++67W577xhtvVE9PT7355psD7q/39fXViBEjVnreX+f7/dy/n/evznHJkiXVNE2NHz/+D/dvsMEG2Vf1f5+f/Jm+vr7adNNN83jbbbdt+fqvX/vmm29q+PDhf3ocWBWiwBrz26v432pra/vb5+688841ceLEmjt3bnV3d9fcuXNryJAh1dXVlT0ffvhhHXDAAbXjjjvWrFmzatSoUTVkyJB64YUXavbs2QOutNeE359jf39/tbW11fz582vQoEED9m+88cbZV1V100031W677faHx/5176/+6HhVVY3/UZd/kCiw1owePbr6+/tryZIltdNOO2X9yy+/rGXLltXo0aNb9nd3d9dFF11Un3/+eT3yyCN12GGHtVw5P/vss/Xjjz/WM88803IV/estmtWd76OPPmq50v/ggw9W+hjjxo2rpmlqzJgxtcMOO/zlvqqq4cOH14EHHrha88Ka4DMF1ppDDz20qqrmzJnTsj5r1qyqqgHf5XP88cdXW1tbXXDBBbV06dKW7zqq+v8r599eKff19dX999+/WvNNnTq1qqruvPPOlvXbbrttpY9x1FFH1aBBg2rmzJkDruCbpqne3t6qqpo4cWKNGzeubr755lq+fPmA43z11VerOj78I7xTYK3Zdddd65RTTql77rmnli1bVp2dnbVw4cJ68MEHa9q0abXffvu17N9yyy3r4IMPrieeeKJGjhw5IBpTpkypIUOG1OGHH15nnXVWLV++vO69997aaqut6vPPP1/l+SZOnFhHH310zZkzp3p7e2uvvfaq1157rd5///2qWrnbXuPGjatrrrmmLr300vr4449r2rRptckmm9RHH31U8+bNqzPPPLNmzJhR7e3tdd9999UhhxxSu+yyS5166qm19dZb12effVavvvpqDR8+vJ599tlVPgf4V4kCa9V9991XY8eOrQceeKDmzZtXHR0ddemll1ZPT88f7u/u7q7nnnuuurq6aujQoS1fmzBhQj355JN1xRVX1IwZM6qjo6POOeec2nLLLeu0005brfkeeuih6ujoqEcffbTmzZtXBx54YD3++OM1YcKE2nDDDVfqGJdcckntsMMONXv27PwcwahRo2rKlCl1xBFHZN/kyZPrzTffrKuvvrpuv/32Wr58eXV0dNSee+5ZZ5111mrND/+qtsanVKzHnn766Zo2bVq9/vrrte+++66TGRYvXly77757zZ07t0488cR1MgOsLT5TYL1277331tixY1v+CYk16YcffhiwNmfOnGpvb69JkyatlRlgXXL7iPXSY489Vm+//XY9//zzdeutt67U/fx/wo033liLFi2q/fbbrwYPHlzz58+v+fPn15lnnlmjRo1aKzPAuuT2Eeultra22njjjevYY4+tu+++uwYPXjvXLy+//HLNnDmz3n333Vq+fHltu+22dfLJJ9fll1++1maAdUkUAAifKQAQogBArPRN0v86/7/X5BwArGEL/+f2v93jnQIAIQoAhCgAEKIAQIgCACEKAIQoABCiAECIAgAhCgCEKAAQogBAiAIAIQoAhCgAEKIAQIgCACEKAIQoABCiAECIAgAhCgCEKAAQogBAiAIAIQoAhCgAEKIAQIgCACEKAIQoABCiAECIAgAhCgCEKAAQogBAiAIAIQoAhCgAEKIAQIgCACEKAIQoABCiAECIAgAhCgCEKAAQogBAiAIAIQoAhCgAEKIAQIgCACEKAIQoABCiAECIAgAhCgCEKAAQogBAiAIAIQoAhCgAEKIAQIgCACEKAIQoABCiAECIAgAhCgCEKAAQogBAiAIAIQoAhCgAEKIAQIgCACEKAIQoABCiAECIAgAhCgCEKAAQogBAiAIAIQoAhCgAEKIAQIgCACEKAIQoABCiAECIAgAhCgCEKAAQogBAiAIAIQoAhCgAEKIAQIgCACEKAIQoABCiAECIAgAhCgCEKAAQogBAiAIAIQoAhCgAEKIAQIgCACEKAIQoABCiAECIAgAhCgCEKAAQogBAiAIAIQoAhCgAEKIAQIgCACEKAIQoABCiAECIAgAhCgCEKAAQogBAiAIAIQoAhCgAEKIAQIgCACEKAIQoABCiAECIAgAhCgCEKAAQogBAiAIAIQoAhCgAEKIAQIgCACEKAIQoABCiAECIAgAhCgCEKAAQogBAiAIAIQoAhCgAEKIAQIgCACEKAIQoABCiAECIAgAhCgCEKAAQogBAiAIAIQoAhCgAEKIAQIgCACEKAIQoABCiAECIAgAhCgCEKAAQogBAiAIAIQoAhCgAEKIAQIgCACEKAIQoABCiAECIAgAhCgCEKAAQogBAiAIAIQoAhCgAEKIAQIgCACEKAIQoABCiAECIAgAhCgCEKAAQogBAiAIAIQoAhCgAEKIAQIgCACEKAIQoABCiAECIAgAhCgCEKAAQogBAiAIAIQoAhCgAEKIAQIgCACEKAIQoABCiAECIAgAhCgCEKAAQogBAiAIAIQoAhCgAEKIAQIgCACEKAIQoABCiAECIAgAhCgCEKAAQogBAiAIAIQoAhCgAEKIAQIgCACEKAIQoABCiAECIAgAhCgCEKAAQogBAiAIAIQoAhCgAEKIAQIgCACEKAIQoABCiAECIAgAhCgCEKAAQogBAiAIAIQoAhCgAEKIAQIgCACEKAIQoABCiAECIAgAhCgCEKAAQogBAiAIAIQoAhCgAEKIAQIgCACEKAIQoABCiAECIAgAhCgCEKAAQogBAiAIAIQoAhCgAEKIAQIgCACEKAIQoABCiAECIAgAhCgCEKAAQogBAiAIAIQoAhCgAEKIAQIgCACEKAIQoABCiAECIAgAhCgCEKAAQogBAiAIAIQoAhCgAEKIAQIgCACEKAIQoABCiAECIAgAhCgCEKAAQogBAiAIAIQoAhCgAEKIAQIgCACEKAIQoABCiAECIAgAhCgCEKAAQogBAiAIAIQoAhCgAEKIAQIgCACEKAIQoABCiAECIAgAhCgCEKAAQogBAiAIAIQoAhCgAEKIAQIgCACEKAIQoABCiAECIAgAhCgCEKAAQogBAiAIAIQoAhCgAEKIAQIgCACEKAIQoABCiAECIAgDR1jRNs66HAGD94J0CACEKAIQoABCiAECIAgAhCgCEKAAQogBAiAIA8b8gTxoHZa4E8gAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_predict_show([\"light green\",\"green\",\"dark green\",\"royal green\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "f1ef8ffd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_predict_show([\"light blue\",\"blue\",\"dark blue\",\"royal blue\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e95c580",
   "metadata": {},
   "source": [
    "## Exporting the model and token_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "a96b4b14",
   "metadata": {},
   "outputs": [],
   "source": [
    "save_dir = \"models/colgen1/v2/\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "1a45bb15",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\panth\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\keras\\src\\engine\\training.py:3000: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
      "  saving_api.save_model(\n"
     ]
    }
   ],
   "source": [
    "import json\n",
    "\n",
    "model.save(save_dir+\"model.h5\")\n",
    "with open(save_dir+\"token_to_idx.txt\",\"w\") as f:\n",
    "    json.dump(token_to_idx,f,indent=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "id": "2b397f49",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5d2ca9cf",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
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  "language_info": {
   "codemirror_mode": {
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    "version": 3
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   "file_extension": ".py",
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