diff --git "a/Colgen1.ipynb" "b/Colgen1.ipynb"
new file mode 100644--- /dev/null
+++ "b/Colgen1.ipynb"
@@ -0,0 +1,997 @@
+{
+ "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": 71,
+   "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": 72,
+   "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": 73,
+   "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": null,
+   "id": "cc60489e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# remove empty name examples"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "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": 203,
+   "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": 159,
+   "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": 164,
+   "id": "038f9a81",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\panth\\AppData\\Local\\Temp\\ipykernel_22052\\2623205928.py:1: DeprecationWarning: NumPy will stop allowing conversion of out-of-bound Python integers to integer arrays.  The conversion of 277 to uint8 will fail in the future.\n",
+      "For the old behavior, usually:\n",
+      "    np.array(value).astype(dtype)`\n",
+      "will give the desired result (the cast overflows).\n",
+      "  train_colors = train_data[:,1:].astype(\"uint8\")\n"
+     ]
+    }
+   ],
+   "source": [
+    "train_colors = train_data[:,1:].astype(\"uint8\")\n",
+    "val_colors = val_data[:,1:].astype(\"uint8\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 174,
+   "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": 174,
+     "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": 256,
+   "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": 259,
+   "id": "1501a0fd",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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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": 193,
+   "id": "47a516d9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model: \"Colgen-1\"\n",
+      "_________________________________________________________________\n",
+      " Layer (type)                Output Shape              Param #   \n",
+      "=================================================================\n",
+      " input_12 (InputLayer)       [(None, None, 37)]        0         \n",
+      "                                                                 \n",
+      " gru_15 (GRU)                (None, None, 32)          6816      \n",
+      "                                                                 \n",
+      " gru_16 (GRU)                (None, None, 64)          18816     \n",
+      "                                                                 \n",
+      " gru_17 (GRU)                (None, 128)               74496     \n",
+      "                                                                 \n",
+      " dense_12 (Dense)            (None, 64)                8256      \n",
+      "                                                                 \n",
+      " dense_13 (Dense)            (None, 32)                2080      \n",
+      "                                                                 \n",
+      " dense_14 (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": 240,
+   "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\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9a12b736",
+   "metadata": {},
+   "source": [
+    "## Training the Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 241,
+   "id": "5eeb102d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/100\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "24/24 [==============================] - 7s 72ms/step - loss: 0.1015 - acc: 0.5497 - val_loss: 0.1191 - val_acc: 0.5278\n",
+      "Epoch 2/100\n",
+      "24/24 [==============================] - 1s 35ms/step - loss: 0.0950 - acc: 0.6464 - val_loss: 0.1099 - val_acc: 0.6597\n",
+      "Epoch 3/100\n",
+      "24/24 [==============================] - 1s 35ms/step - loss: 0.0874 - acc: 0.6887 - val_loss: 0.0990 - val_acc: 0.6667\n",
+      "Epoch 4/100\n",
+      "24/24 [==============================] - 1s 37ms/step - loss: 0.0826 - acc: 0.7417 - val_loss: 0.0952 - val_acc: 0.7222\n",
+      "Epoch 5/100\n",
+      "24/24 [==============================] - 1s 34ms/step - loss: 0.0795 - acc: 0.7603 - val_loss: 0.0911 - val_acc: 0.7500\n",
+      "Epoch 6/100\n",
+      "24/24 [==============================] - 1s 34ms/step - loss: 0.0763 - acc: 0.7682 - val_loss: 0.0888 - val_acc: 0.7569\n",
+      "Epoch 7/100\n",
+      "24/24 [==============================] - 1s 34ms/step - loss: 0.0736 - acc: 0.7801 - val_loss: 0.0856 - val_acc: 0.7639\n",
+      "Epoch 8/100\n",
+      "24/24 [==============================] - 1s 35ms/step - loss: 0.0710 - acc: 0.7801 - val_loss: 0.0844 - val_acc: 0.7500\n",
+      "Epoch 9/100\n",
+      "24/24 [==============================] - 1s 34ms/step - loss: 0.0688 - acc: 0.7775 - val_loss: 0.0842 - val_acc: 0.7292\n",
+      "Epoch 10/100\n",
+      "24/24 [==============================] - 1s 36ms/step - loss: 0.0672 - acc: 0.7682 - val_loss: 0.0819 - val_acc: 0.7431\n",
+      "Epoch 11/100\n",
+      "24/24 [==============================] - 1s 35ms/step - loss: 0.0656 - acc: 0.7801 - val_loss: 0.0809 - val_acc: 0.7500\n",
+      "Epoch 12/100\n",
+      "24/24 [==============================] - 1s 34ms/step - loss: 0.0644 - acc: 0.7709 - val_loss: 0.0803 - val_acc: 0.7361\n",
+      "Epoch 13/100\n",
+      "24/24 [==============================] - 1s 35ms/step - loss: 0.0627 - acc: 0.7788 - val_loss: 0.0787 - val_acc: 0.7361\n",
+      "Epoch 14/100\n",
+      "24/24 [==============================] - 1s 35ms/step - loss: 0.0617 - acc: 0.7748 - val_loss: 0.0798 - val_acc: 0.7222\n",
+      "Epoch 15/100\n",
+      "24/24 [==============================] - 1s 34ms/step - loss: 0.0606 - acc: 0.7762 - val_loss: 0.0803 - val_acc: 0.7361\n",
+      "Epoch 16/100\n",
+      "24/24 [==============================] - 1s 34ms/step - loss: 0.0598 - acc: 0.7735 - val_loss: 0.0793 - val_acc: 0.7431\n",
+      "Epoch 17/100\n",
+      "24/24 [==============================] - 1s 35ms/step - loss: 0.0589 - acc: 0.7709 - val_loss: 0.0806 - val_acc: 0.7500\n",
+      "Epoch 18/100\n",
+      "24/24 [==============================] - 1s 35ms/step - loss: 0.0583 - acc: 0.7656 - val_loss: 0.0724 - val_acc: 0.7569\n",
+      "Epoch 19/100\n",
+      "24/24 [==============================] - 1s 34ms/step - loss: 0.0557 - acc: 0.7775 - val_loss: 0.0693 - val_acc: 0.7917\n",
+      "Epoch 20/100\n",
+      "24/24 [==============================] - 1s 35ms/step - loss: 0.0527 - acc: 0.7894 - val_loss: 0.0677 - val_acc: 0.7917\n",
+      "Epoch 21/100\n",
+      "24/24 [==============================] - 1s 35ms/step - loss: 0.0502 - acc: 0.8026 - val_loss: 0.0654 - val_acc: 0.7778\n",
+      "Epoch 22/100\n",
+      "24/24 [==============================] - 1s 35ms/step - loss: 0.0482 - acc: 0.8066 - val_loss: 0.0620 - val_acc: 0.7708\n",
+      "Epoch 23/100\n",
+      "24/24 [==============================] - 1s 35ms/step - loss: 0.0466 - acc: 0.8079 - val_loss: 0.0605 - val_acc: 0.7708\n",
+      "Epoch 24/100\n",
+      "24/24 [==============================] - 1s 36ms/step - loss: 0.0441 - acc: 0.8146 - val_loss: 0.0599 - val_acc: 0.7778\n",
+      "Epoch 25/100\n",
+      "24/24 [==============================] - 1s 35ms/step - loss: 0.0422 - acc: 0.8132 - val_loss: 0.0582 - val_acc: 0.7917\n",
+      "Epoch 26/100\n",
+      "24/24 [==============================] - 1s 36ms/step - loss: 0.0408 - acc: 0.8119 - val_loss: 0.0557 - val_acc: 0.7778\n",
+      "Epoch 27/100\n",
+      "24/24 [==============================] - 1s 35ms/step - loss: 0.0391 - acc: 0.8146 - val_loss: 0.0547 - val_acc: 0.7708\n",
+      "Epoch 28/100\n",
+      "24/24 [==============================] - 1s 35ms/step - loss: 0.0383 - acc: 0.8185 - val_loss: 0.0576 - val_acc: 0.7778\n",
+      "Epoch 29/100\n",
+      "24/24 [==============================] - 1s 35ms/step - loss: 0.0369 - acc: 0.8026 - val_loss: 0.0591 - val_acc: 0.7917\n",
+      "Epoch 30/100\n",
+      "24/24 [==============================] - 1s 35ms/step - loss: 0.0357 - acc: 0.8212 - val_loss: 0.0543 - val_acc: 0.7847\n",
+      "Epoch 31/100\n",
+      "24/24 [==============================] - 1s 35ms/step - loss: 0.0339 - acc: 0.8185 - val_loss: 0.0535 - val_acc: 0.7708\n",
+      "Epoch 32/100\n",
+      "24/24 [==============================] - 1s 35ms/step - loss: 0.0342 - acc: 0.8159 - val_loss: 0.0488 - val_acc: 0.7500\n",
+      "Epoch 33/100\n",
+      "24/24 [==============================] - 1s 35ms/step - loss: 0.0344 - acc: 0.8344 - val_loss: 0.0482 - val_acc: 0.7639\n",
+      "Epoch 34/100\n",
+      "24/24 [==============================] - 1s 35ms/step - loss: 0.0352 - acc: 0.8159 - val_loss: 0.0458 - val_acc: 0.7569\n",
+      "Epoch 35/100\n",
+      "24/24 [==============================] - 1s 36ms/step - loss: 0.0326 - acc: 0.8132 - val_loss: 0.0410 - val_acc: 0.7708\n",
+      "Epoch 36/100\n",
+      "24/24 [==============================] - 1s 35ms/step - loss: 0.0290 - acc: 0.8331 - val_loss: 0.0401 - val_acc: 0.7500\n",
+      "Epoch 37/100\n",
+      "24/24 [==============================] - 1s 35ms/step - loss: 0.0258 - acc: 0.8238 - val_loss: 0.0389 - val_acc: 0.7778\n",
+      "Epoch 38/100\n",
+      "24/24 [==============================] - 1s 36ms/step - loss: 0.0230 - acc: 0.8464 - val_loss: 0.0363 - val_acc: 0.7292\n",
+      "Epoch 39/100\n",
+      "24/24 [==============================] - 1s 36ms/step - loss: 0.0213 - acc: 0.8397 - val_loss: 0.0350 - val_acc: 0.7708\n",
+      "Epoch 40/100\n",
+      "24/24 [==============================] - 1s 35ms/step - loss: 0.0203 - acc: 0.8450 - val_loss: 0.0365 - val_acc: 0.7361\n",
+      "Epoch 41/100\n",
+      "24/24 [==============================] - 1s 35ms/step - loss: 0.0193 - acc: 0.8517 - val_loss: 0.0373 - val_acc: 0.7847\n",
+      "Epoch 42/100\n",
+      "24/24 [==============================] - 1s 35ms/step - loss: 0.0200 - acc: 0.8490 - val_loss: 0.0398 - val_acc: 0.7639\n",
+      "Epoch 43/100\n",
+      "24/24 [==============================] - 1s 35ms/step - loss: 0.0207 - acc: 0.8318 - val_loss: 0.0431 - val_acc: 0.7708\n",
+      "Epoch 44/100\n",
+      "24/24 [==============================] - 1s 36ms/step - loss: 0.0200 - acc: 0.8344 - val_loss: 0.0390 - val_acc: 0.7431\n",
+      "Epoch 45/100\n",
+      "24/24 [==============================] - 1s 35ms/step - loss: 0.0192 - acc: 0.8530 - val_loss: 0.0375 - val_acc: 0.7917\n",
+      "Epoch 46/100\n",
+      "24/24 [==============================] - 1s 37ms/step - loss: 0.0185 - acc: 0.8503 - val_loss: 0.0353 - val_acc: 0.7639\n",
+      "Epoch 47/100\n",
+      "24/24 [==============================] - 1s 35ms/step - loss: 0.0173 - acc: 0.8570 - val_loss: 0.0340 - val_acc: 0.7708\n",
+      "Epoch 48/100\n",
+      "24/24 [==============================] - 1s 35ms/step - loss: 0.0173 - acc: 0.8331 - val_loss: 0.0313 - val_acc: 0.7500\n",
+      "Epoch 49/100\n",
+      "24/24 [==============================] - 1s 35ms/step - loss: 0.0192 - acc: 0.8490 - val_loss: 0.0329 - val_acc: 0.7778\n",
+      "Epoch 50/100\n",
+      "24/24 [==============================] - 1s 36ms/step - loss: 0.0205 - acc: 0.8543 - val_loss: 0.0346 - val_acc: 0.7153\n",
+      "Epoch 51/100\n",
+      "24/24 [==============================] - 1s 35ms/step - loss: 0.0201 - acc: 0.8490 - val_loss: 0.0363 - val_acc: 0.7708\n",
+      "Epoch 52/100\n",
+      "24/24 [==============================] - 1s 36ms/step - loss: 0.0172 - acc: 0.8464 - val_loss: 0.0317 - val_acc: 0.7569\n",
+      "Epoch 53/100\n",
+      "24/24 [==============================] - 1s 35ms/step - loss: 0.0159 - acc: 0.8556 - val_loss: 0.0328 - val_acc: 0.8125\n",
+      "Epoch 54/100\n",
+      "24/24 [==============================] - 1s 35ms/step - loss: 0.0132 - acc: 0.8464 - val_loss: 0.0283 - val_acc: 0.7986\n",
+      "Epoch 55/100\n",
+      "24/24 [==============================] - 1s 35ms/step - loss: 0.0114 - acc: 0.8649 - val_loss: 0.0269 - val_acc: 0.8542\n",
+      "Epoch 56/100\n",
+      "24/24 [==============================] - 1s 36ms/step - loss: 0.0101 - acc: 0.8477 - val_loss: 0.0246 - val_acc: 0.8333\n",
+      "Epoch 57/100\n",
+      "24/24 [==============================] - 1s 35ms/step - loss: 0.0095 - acc: 0.8344 - val_loss: 0.0223 - val_acc: 0.7569\n",
+      "Epoch 58/100\n",
+      "24/24 [==============================] - 1s 35ms/step - loss: 0.0086 - acc: 0.8662 - val_loss: 0.0242 - val_acc: 0.7292\n",
+      "Epoch 59/100\n",
+      "24/24 [==============================] - 1s 35ms/step - loss: 0.0082 - acc: 0.8596 - val_loss: 0.0255 - val_acc: 0.7500\n",
+      "Epoch 60/100\n",
+      "24/24 [==============================] - 1s 35ms/step - loss: 0.0084 - acc: 0.8675 - val_loss: 0.0228 - val_acc: 0.7708\n",
+      "Epoch 61/100\n",
+      "24/24 [==============================] - 1s 35ms/step - loss: 0.0069 - acc: 0.8927 - val_loss: 0.0250 - val_acc: 0.8194\n",
+      "Epoch 62/100\n",
+      "24/24 [==============================] - 1s 35ms/step - loss: 0.0063 - acc: 0.8914 - val_loss: 0.0242 - val_acc: 0.8264\n",
+      "Epoch 63/100\n",
+      "24/24 [==============================] - 1s 35ms/step - loss: 0.0062 - acc: 0.8834 - val_loss: 0.0226 - val_acc: 0.7986\n",
+      "Epoch 64/100\n",
+      "24/24 [==============================] - 1s 35ms/step - loss: 0.0058 - acc: 0.8914 - val_loss: 0.0214 - val_acc: 0.7639\n",
+      "Epoch 65/100\n",
+      "24/24 [==============================] - 1s 36ms/step - loss: 0.0057 - acc: 0.8834 - val_loss: 0.0208 - val_acc: 0.7431\n",
+      "Epoch 66/100\n",
+      "24/24 [==============================] - 1s 36ms/step - loss: 0.0057 - acc: 0.8993 - val_loss: 0.0216 - val_acc: 0.7639\n",
+      "Epoch 67/100\n",
+      "24/24 [==============================] - 1s 35ms/step - loss: 0.0057 - acc: 0.9007 - val_loss: 0.0212 - val_acc: 0.7847\n",
+      "Epoch 68/100\n",
+      "24/24 [==============================] - 1s 35ms/step - loss: 0.0055 - acc: 0.8940 - val_loss: 0.0236 - val_acc: 0.7986\n",
+      "Epoch 69/100\n",
+      "24/24 [==============================] - 1s 35ms/step - loss: 0.0059 - acc: 0.8834 - val_loss: 0.0247 - val_acc: 0.8194\n",
+      "Epoch 70/100\n",
+      "24/24 [==============================] - 1s 36ms/step - loss: 0.0070 - acc: 0.8795 - val_loss: 0.0220 - val_acc: 0.8194\n",
+      "Epoch 71/100\n",
+      "24/24 [==============================] - 1s 40ms/step - loss: 0.0070 - acc: 0.8940 - val_loss: 0.0279 - val_acc: 0.7847\n",
+      "Epoch 72/100\n",
+      "24/24 [==============================] - 1s 36ms/step - loss: 0.0077 - acc: 0.8689 - val_loss: 0.0246 - val_acc: 0.7500\n",
+      "Epoch 73/100\n",
+      "24/24 [==============================] - 1s 38ms/step - loss: 0.0071 - acc: 0.8834 - val_loss: 0.0234 - val_acc: 0.7639\n",
+      "Epoch 74/100\n",
+      "24/24 [==============================] - 1s 36ms/step - loss: 0.0066 - acc: 0.8887 - val_loss: 0.0225 - val_acc: 0.7847\n",
+      "Epoch 75/100\n",
+      "24/24 [==============================] - 1s 35ms/step - loss: 0.0068 - acc: 0.8874 - val_loss: 0.0258 - val_acc: 0.8403\n",
+      "Epoch 76/100\n",
+      "24/24 [==============================] - 1s 37ms/step - loss: 0.0058 - acc: 0.8914 - val_loss: 0.0216 - val_acc: 0.8333\n",
+      "Epoch 77/100\n",
+      "24/24 [==============================] - 1s 38ms/step - loss: 0.0053 - acc: 0.9046 - val_loss: 0.0180 - val_acc: 0.8125\n",
+      "Epoch 78/100\n",
+      "24/24 [==============================] - 1s 37ms/step - loss: 0.0051 - acc: 0.8993 - val_loss: 0.0198 - val_acc: 0.7847\n",
+      "Epoch 79/100\n",
+      "24/24 [==============================] - 1s 37ms/step - loss: 0.0046 - acc: 0.8927 - val_loss: 0.0193 - val_acc: 0.7361\n",
+      "Epoch 80/100\n",
+      "24/24 [==============================] - 1s 37ms/step - loss: 0.0045 - acc: 0.8954 - val_loss: 0.0203 - val_acc: 0.7847\n",
+      "Epoch 81/100\n",
+      "24/24 [==============================] - 1s 36ms/step - loss: 0.0042 - acc: 0.8821 - val_loss: 0.0215 - val_acc: 0.8194\n",
+      "Epoch 82/100\n",
+      "24/24 [==============================] - 1s 36ms/step - loss: 0.0045 - acc: 0.9046 - val_loss: 0.0221 - val_acc: 0.8333\n",
+      "Epoch 83/100\n",
+      "24/24 [==============================] - 1s 43ms/step - loss: 0.0048 - acc: 0.9007 - val_loss: 0.0227 - val_acc: 0.7986\n",
+      "Epoch 84/100\n",
+      "24/24 [==============================] - 1s 41ms/step - loss: 0.0053 - acc: 0.9020 - val_loss: 0.0218 - val_acc: 0.7431\n",
+      "Epoch 85/100\n",
+      "24/24 [==============================] - 1s 39ms/step - loss: 0.0053 - acc: 0.8967 - val_loss: 0.0241 - val_acc: 0.8194\n",
+      "Epoch 86/100\n",
+      "24/24 [==============================] - 1s 39ms/step - loss: 0.0055 - acc: 0.9033 - val_loss: 0.0212 - val_acc: 0.8125\n",
+      "Epoch 87/100\n",
+      "24/24 [==============================] - 1s 37ms/step - loss: 0.0057 - acc: 0.8993 - val_loss: 0.0207 - val_acc: 0.8681\n",
+      "Epoch 88/100\n",
+      "24/24 [==============================] - 1s 35ms/step - loss: 0.0060 - acc: 0.8940 - val_loss: 0.0238 - val_acc: 0.8194\n",
+      "Epoch 89/100\n",
+      "24/24 [==============================] - 1s 36ms/step - loss: 0.0058 - acc: 0.8874 - val_loss: 0.0237 - val_acc: 0.7361\n",
+      "Epoch 90/100\n",
+      "24/24 [==============================] - 1s 36ms/step - loss: 0.0064 - acc: 0.8940 - val_loss: 0.0223 - val_acc: 0.7292\n",
+      "Epoch 91/100\n",
+      "24/24 [==============================] - 1s 35ms/step - loss: 0.0071 - acc: 0.8781 - val_loss: 0.0239 - val_acc: 0.7778\n",
+      "Epoch 92/100\n",
+      "24/24 [==============================] - 1s 36ms/step - loss: 0.0063 - acc: 0.8993 - val_loss: 0.0235 - val_acc: 0.8264\n",
+      "Epoch 93/100\n",
+      "24/24 [==============================] - 1s 36ms/step - loss: 0.0057 - acc: 0.8781 - val_loss: 0.0200 - val_acc: 0.8264\n",
+      "Epoch 94/100\n",
+      "24/24 [==============================] - 1s 35ms/step - loss: 0.0053 - acc: 0.8980 - val_loss: 0.0176 - val_acc: 0.7986\n",
+      "Epoch 95/100\n",
+      "24/24 [==============================] - 1s 36ms/step - loss: 0.0044 - acc: 0.9139 - val_loss: 0.0182 - val_acc: 0.7569\n",
+      "Epoch 96/100\n",
+      "24/24 [==============================] - 1s 35ms/step - loss: 0.0037 - acc: 0.9073 - val_loss: 0.0180 - val_acc: 0.8194\n",
+      "Epoch 97/100\n",
+      "24/24 [==============================] - 1s 36ms/step - loss: 0.0035 - acc: 0.9099 - val_loss: 0.0185 - val_acc: 0.8056\n",
+      "Epoch 98/100\n",
+      "24/24 [==============================] - 1s 35ms/step - loss: 0.0029 - acc: 0.9113 - val_loss: 0.0179 - val_acc: 0.8472\n",
+      "Epoch 99/100\n",
+      "24/24 [==============================] - 1s 36ms/step - loss: 0.0030 - acc: 0.9046 - val_loss: 0.0187 - val_acc: 0.8125\n",
+      "Epoch 100/100\n",
+      "24/24 [==============================] - 1s 35ms/step - loss: 0.0029 - acc: 0.9152 - val_loss: 0.0209 - val_acc: 0.7500\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<keras.src.callbacks.History at 0x1d937fb4d50>"
+      ]
+     },
+     "execution_count": 241,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bs=32\n",
+    "train_ds = get_tf_dataset(train_name_tokens,train_colors,bs=bs)\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": 242,
+   "id": "ba282280",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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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": 243,
+   "id": "9d613e0b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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HHXaIiMj8f3mVt1baUNdcc03k5eXFJZdcEnPmzInhw4fHAQccEJMnT846lvPnz4+DDz44jj/++PjlL38ZW221VRQVFcXhhx8er7zySgwYMCB22GGHmDp1avzhD3+Ijz76KMaNG5fZ/pRTTon77rsvevfuHZ06dYoXXnhhnfXUz3/+81IvXgMAsOG2tDrj5Zdfjscffzyzv+uuuy4OPfTQuPjii+OWW26JM888MxYuXBjXX399nHTSSfHCCy9kti1vXfHUU0/FcccdFzvvvHNcd911sXDhwjj55JNL/Ubw0047LUaPHh39+/ePc889N2bMmBE333xzvP322/Hqq69GlSpVMn0/+eSTOOaYY+Lkk0+Ovn37xl133RX9+vWLDh06RPv27ddbH4wePTpq1qwZF1xwQdSsWTNeeOGFuOqqq+LLL7+MG264YZ3H7bXXXouIKLW+Ke91jsGDB8egQYOiU6dOMWTIkKhatWq88cYb8cILL0T37t1j+PDhcc4550TNmjXjiiuuiIgo9XW0Pt9lnWVZuHBhHHLIIdGrV6844YQTYsyYMXHGGWdE1apV46STTsrqO3To0KhatWpcdNFFsWLFiqhatWq5r49tyO9TxLfXXVJK8dprr8Whhx66UWvbYiU22pFHHpkKCwvTrFmzMm3Tpk1LlSpVSmsf2qVLl5bYvkePHql169ZZbS1atEgRkcaPH1+if0Sks846K6WU0oUXXpjy8/PT6NGj1zvPSZMmpYhI5513XlZ7v379UkSkgQMHZtoGDhyYIiKdcMIJJcYpbQ1//etfU0Skv//975m2yy67LBUUFKRFixZl2ubMmZMqV66cta/S3HHHHSki0tSpU8u1/7WP4aJFi1KtWrXSnnvumZYtW5bVt6ioKPPfPXv2TC1atCgx3qhRo1JEpBkzZmS1T5w4MUVEmjhx4jrnc91116W8vLys10TxMV2fvn37pohI55xzTtace/bsmapWrZrmzp2bUkppxowZKSJS7dq105w5c7LG6NatW9p5553T8uXLs8bo1KlT2m677TJt5513XoqI9MYbb2Ta5syZk+rUqVPq+lNKqWrVqumMM85Y7zoAANj8cqkWmTlzZqpUqVK65pprstqnTp2aKleunGkvKipK2223XerRo0fWuf/SpUtTq1at0oEHHlhiXyeddFLWmEcddVRq0KDBete1zz77pA4dOpRoX758eVq9enVW24wZM1JBQUEaMmRIpu2uu+5KEZFuvPHGEmMUz33u3LkljlGxLl26pC5dupRo79u3b4k6Z+3nf+XKlWmnnXZK+++/f1Z7ixYtUt++fUuMubaISBGR3nzzzUzbrFmzUmFhYTrqqKMybd/1+Vy5cmVq3Lhx2mWXXdKKFSsy/UaOHJkiotT1/+UvfylR5wAA8P3ZUuqU8m5bfF1ghx12yDonHTFiRInrGOW9XtC3b99Uo0aNlFJKr7zySqpdu3bq2bNn1nv6KZV9zr+mb775JuXl5aULL7ywxGPlvZ7Tp0+flJ+fn/71r3+V6F9cm4wdO7bE9ZFiZdUsa9cX5a2Viq9/jBo1qsSYayp+bpo1a5a+/PLLTPuYMWNSRKQRI0Zk2rp06ZIiIt12221ZY9x7770pPz8/vfzyy1ntt912W4qI9Oqrr6aUUpo8eXKKiHTmmWdm9evdu3eZ6x8wYECqVq3aOtcAAED5bGl1RkFBQVbm5vbbb08RkZo0aZJ17nrZZZeVyOeUt67Yeeed0zbbbJO++uqrTNuLL76YIiLrGsHLL7+cIiLdf//9WWOOHz++RHvxMVmzXpgzZ04qKCjIqjnWVR+UNv/TTjstVa9evUTNs7Yrr7wyRUTWmsoat7TrHB9//HHKz89PRx11VInaY81rRu3bty+11ior21Vajqy86yztmk1pimuW//f//l+mbcWKFWmXXXZJjRs3TitXrkwp/V8d1Lp166w5bMj1sQ35fUoppc8//zxFRBo2bNh61/Fjk78Jcrk5afXq1fHMM8/EkUceGc2bN8+077DDDtGjR48S/df8ROzixYtj3rx50aVLl/j0009j8eLFWX1btWpV6hgRESmlOPvss2PEiBFx3333Rd++fdc71/Hjx0dExJlnnpnVfs4555S5zemnn77ONSxfvjzmzZsXHTt2jIiIt956K/NYnz59YsWKFVm32n7wwQdj1apV8ctf/nKdcy3+6qF69eqtc/9lHcMJEybEV199FZdeemkUFhZmbV/abbq/izXn8/XXX8e8efOiU6dOkVKKt99+e6PHPfvsszP/XXxr+JUrV8Zzzz2X1e/oo4/OfCVsxLdfn/TCCy9Er1694quvvop58+bFvHnzYv78+dGjR4/4+OOPY/bs2RER8be//S06duyYdaeuRo0axYknnljmvOrVqxfz5s3b6HUBALBp5Fot8sgjj0RRUVH06tUrc447b968aNKkSWy33XYxceLEiPj2K1M//vjj6N27d8yfPz/T7+uvv45u3brF3//+9ygqKlrnvjp37hzz58/PfFVOWebPn19qzVJQUBD5+d+W2atXr4758+dHzZo1Y/vtt8+qmR5++OFo2LBhqcdhc9YtCxcujMWLF0fnzp2z5rOh9tprr8y3WURENG/ePI444oh45plnSny90cY+n2+++WbMmTMnTj/99KhatWpm+379+kWdOnVKnVfxc6JuAQD4/m1JdcqGbtu/f/+sc9LOnTtHxLd3Xy1tPeW5XjBx4sTo0aNHdOvWLR555JEoKCgo97yLLViwIFJK672eUtb1nKKiohg3blwcdthhsdtuu5UYY1PWJuWtlTZUnz59olatWpmfjznmmNh6663jb3/7W4n99+/fP6tt7NixscMOO0S7du2yapP9998/IiJTmxSPde6552Ztf95555U5r3r16sWyZcti6dKlG702AAC2zDqjW7du0bJly8zPe+65Z0R8m/FZ89y1uH1D64rPP/88pk6dGn369Mn6Vu4uXbrEzjvvnDWXsWPHRp06deLAAw/MOuft0KFD1KxZM3POW2zHHXfM1DsR3+aItt9++6w5rsua8y/OLXXu3DmWLl0aH3zwwTq3nT9/flSuXLnUbxovz3WOcePGRVFRUVx11VWZ2qPY5rzusqHrLEvlypXjtNNOy/xctWrVOO2002LOnDkxadKkrL59+/bNmkN5r49t6O9TRG5fd6lc0RPYUs2dOzeWLVsW2223XYnHtt9++xIF+6uvvhoDBw6M119/vUQRvXjx4qyLgq1atSpzv/fcc08sWbIkbr311jjhhBPKNddZs2ZFfn5+iXHbtm1b5jalzWHBggUxePDgeOCBB2LOnDkl1lCsXbt2sfvuu8f9998fJ598ckRE3H///dGxY8d17nNN6f//WtA1lecYTp8+PSKixFeObg7//ve/46qrrorHH388Fi5cWGI+GyM/Pz9at26d1faTn/wkIiJmzpyZ1b72c/TJJ59ESil++9vfxm9/+9tSx58zZ040a9YsZs2alfkHek1l3c4+4tvnZFP/QwMAwIbLtVrk448/jpRSqeuNiMxXE3388ccREet8c2vx4sVZF5zXfNMg4v/eHFi4cGHUrl27zHEiSq9ZioqKYsSIEXHLLbfEjBkzsgKmDRo0yPz39OnTY/vtt4/KlTd/Sf7kk0/G1VdfHZMnT44VK1Zk2r/LuX1pz8VPfvKTWLp0acydOzeaNGmSad/Y53PWrFml7qtKlSolaqZixc+JugUA4Pu3JdUpG7rtuuqGYhtyvWD58uXRs2fP6NChQ4wZM+Y71wWl1SbluZ4zd+7c+PLLL7+X6ynlrZU21Nqvt7y8vGjbtm2J6ynNmjXLCkZHfFubvP/++1k3L1lT8XErrmvbtGmT9fj6rqcUzwcAgI23JdYZa9cPxfvcdtttS23f0Lqi+L3z0q6ztG3bNito+vHHH8fixYujcePGpc517Vph7blHfFv/rD2Xsrz33ntx5ZVXxgsvvFDi5iUbm6OKKN91junTp0d+fn7suOOOG72f8toc62zatGnUqFEjq23NvFjxhyQjSr/uErH+62MrVqzYoN+niNyubYRqvwfTp0+Pbt26Rbt27eLGG2+MbbfdNqpWrRp/+9vf4g9/+EOJuyWtmSZf29577x2TJ0+Om2++OXr16hX169ffLHMubQ69evWK1157LX7zm9/ELrvsEjVr1oyioqI46KCDSqyhT58+8etf/zo+++yzWLFiRfzjH/+Im2++eb37LX7zZOHChbHNNttk2jf0GG6ssv4IrH23pdWrV8eBBx4YCxYsiEsuuSTatWsXNWrUiNmzZ0e/fv022XzWZe3nqHifF110UZmfIChvqLk0ixYtioYNG2709gAAfP9+DLVIUVFR5OXlxdNPPx2VKlUq0b/4U8vFa7nhhhtil112KXXstT/hXNp4EaVflF5TgwYNSn0T6dprr43f/va3cdJJJ8XQoUOjfv36kZ+fH+edd94mrRHy8vJKnePadcvLL78chx9+eOy7775xyy23xNZbbx1VqlSJUaNGxV/+8pdNNp912djnc2MUPyfqFgCAH7YfSp1S3m3XVzds6PWCgoKCOOSQQ+Kxxx6L8ePHx6GHHlruOa+pfv36kZeXV2ptsiHXczaHtWuT76tWKktpr6GioqLYeeed48Ybbyx1m7VDDxti4cKFUb169XW+dgEA2LR+KHVGWfXDpq4ryqOoqCgaN24c999/f6mPr/0Bs429ZhLxbaaoS5cuUbt27RgyZEi0adMmCgsL46233opLLrlkvfNv0KBBrFq1Kr766qusO/p+X9c5ypsX+67r3BTKyout7/rYmoHk8srl6y5CtRupUaNGUa1atUzae00ffvhh1s9PPPFErFixIh5//PGsVP/at9Euj7Zt28b1118fXbt2jYMOOiief/75rD8mpWnRokUUFRXFjBkzstLmn3zySbn3u3Dhwnj++edj8ODBcdVVV2XaS1t/RMTxxx8fF1xwQfz1r3+NZcuWRZUqVeK4445b737atWsXEREzZszIui15eY9h8SeF33333XUGSMv6Y1j8CfNFixZltRd/0qPY1KlT46OPPoq77747+vTpk2mfMGFCmfssj6Kiovj0008znzaIiPjoo48iIrJuD1+a4rs1ValSJQ444IB19m3RokW5XrvFZs+eHStXrowddthhneMCALD55Vot0qZNm0gpRatWrbLOk0vrFxFRu3bt9Z4Pf1ft2rWLhx9+uET7Qw89FPvtt1/ceeedWe1rf0CtTZs28cYbb8Q333yTuTPr2tb1qd969eqV+nVLa9ctDz/8cBQWFsYzzzyT9XWyo0aNKnPs8ijttffRRx9F9erVy7zLU7HyPp8tWrTI7Kv461cjIr755puYMWNG/OxnPyuxzYwZMyI/P3+d4wIAsHlsSXXKpth2TRt6vSAvLy/uv//+OOKII+LYY4+Np59+Orp27brB+61cuXK0adMmZsyYkdVe3us5jRo1itq1a8e77767zv2srzZZ+3rKypUr47///W9WW3lrpQ219ppSSvHJJ5/ET3/60/Vu26ZNm5gyZUp069ZtnWssrmuLv3GkWFnXUyK+rU1cTwEA+O62xDpjY5W3rih+77y06yxrt7Vp0yaee+652HvvvTfZB77KOnd+8cUXY/78+fHII4/Evvvum2lfu14py5p5sTXP58t7naNNmzZRVFQU06ZNKzNYuq75r5kXq1u3bqZ97esu33WdZfn888/j66+/zrpbbXnzYuW9PrYhv0/FiteVi/VNfkVPYEtVqVKl6NGjR4wbNy7+/e9/Z9rff//9eOaZZ0r0jchO7i9evHijL2T+9Kc/jb/97W/x/vvvx2GHHRbLli1bZ//iu5becsstWe033XRTufdZ2hoiIoYPH15q/4YNG8bBBx8c9913X9x///1x0EEHleuNkQ4dOkTVqlXjzTffXO/+SzuG3bt3j1q1asV1110Xy5cvz3pszW1r1KhR6i23i//Q/P3vf8+0rV69OkaOHLne+aSUYsSIEetd4/qseUfflFLcfPPNUaVKlejWrds6t2vcuHF07do1br/99hJvWEV8e1v8Yoccckj84x//iH/+859Zj5f16ZRJkyZFRESnTp02aC0AAGx6uVaL/OIXv4hKlSrF4MGDS9QjKaWYP39+RHxbS7Rp0yZ+//vfx5IlS0qMs+b58He11157xcKFC0sEWytVqlRijmPHjo3Zs2dntR199NExb968Ur/No3j76tWrR0TJD/xFfFu3fPDBB1lrmjJlSrz66qsl5pOXl5f1SeqZM2fGuHHj1r/IdXj99dezvkLqP//5Tzz22GPRvXv3Mj/JXqy8z+duu+0WjRo1ittuuy1WrlyZ6TN69OhSj0nEt3VL+/bts77CCwCA78eWVKdsqm2Lbcz1gqpVq8YjjzwSu+++exx22GFZ79VviL322qtc11MiSl7Pyc/PjyOPPDKeeOKJEmOsuX3xRd2yapM1r6dERIwcObLE3ZzKWyttqHvuuSe++uqrzM8PPfRQ/Pe//42DDz54vdv26tUrZs+eHX/+859LPLZs2bL4+uuvIyIyY/3xj3/M6lPW9bGIiLfeesv1FACATWBLrTM2RnnriqZNm8ZOO+0U99xzT9a1kJdeeimmTp2a1bdXr16xevXqGDp0aIn9rVq1qsz32telrPqgtPmvXLmyxPWhsuy1114REaXWN+W5znHkkUdGfn5+DBkypMTdYtfOi5VV20Rk58W+/vrruPvuu0vMZ+0xN2SdZVm1alXcfvvtWWPefvvt0ahRo+jQocM6ty3v9bEN+X0qNmnSpMjLy8s8P7nEnWq/g8GDB8f48eOjc+fOceaZZ8aqVavipptuivbt28c777yT6de9e/eoWrVqHHbYYXHaaafFkiVL4s9//nM0bty41PBjeXTs2DEee+yxOOSQQ+KYY46JcePGlXmHow4dOsTRRx8dw4cPj/nz50fHjh3jpZdeyiTa1/UJ3GK1a9eOfffdN66//vr45ptvolmzZvHss8+uM2nfp0+fOOaYYyIiSv0DXZrCwsLo3r17PPfcczFkyJBMe3mPYe3ateMPf/hDnHLKKbH77rtH7969o169ejFlypRYunRp5o9dhw4d4sEHH4wLLrggdt9996hZs2Ycdthh0b59++jYsWNcdtllsWDBgqhfv3488MADsWrVqqx5tmvXLtq0aRMXXXRRzJ49O2rXrh0PP/xwqV+zVJpBgwbF4MGDY+LEiVmfQC8sLIzx48dH3759Y88994ynn346nnrqqbj88svXe8eniIg//elPsc8++8TOO+8cp556arRu3Tq++OKLeP311+Ozzz6LKVOmRETExRdfHPfee28cdNBB8etf/zpq1KgRI0eOjBYtWmS9dotNmDAhmjdvHrvuumu51gcAwOaVS7VImzZt4uqrr47LLrssZs6cGUceeWTUqlUrZsyYEY8++mgMGDAgLrroosjPz4877rgjDj744Gjfvn30798/mjVrFrNnz46JEydG7dq144knntioNa+tZ8+eUbly5XjuuediwIABmfZDDz00hgwZEv37949OnTrF1KlT4/777898q0SxPn36xD333BMXXHBB/POf/4zOnTvH119/Hc8991yceeaZccQRR0S1atVixx13jAcffDB+8pOfRP369WOnnXaKnXbaKU466aS48cYbo0ePHnHyySfHnDlz4rbbbov27dvHl19+mTXPG2+8MQ466KDo3bt3zJkzJ/70pz9F27ZtSz3vX1vxp59nzpyZ1b7TTjtFjx494txzz42CgoLMm0WDBw9e75jlfT6rVKkSV199dZx22mmx//77x3HHHRczZsyIUaNGlTieEd/ewfall16KM888c71zAABg89hS6pRNuW3Exl8vqFatWjz55JOx//77x8EHHxwvvfRS7LTTTuXeb0TEEUccEffee2989NFHmW9s2JDrOddee208++yz0aVLlxgwYEDssMMO8d///jfGjh0br7zyStStWzd22WWXqFSpUgwbNiwWL14cBQUFsf/++0fjxo3jlFNOidNPPz2OPvroOPDAA2PKlCnxzDPPlLjJSnlrpdLMnDkzWrVqFX379o3Ro0dnPVa/fv3YZ599on///vHFF1/E8OHDo23btnHqqaeud9xf/epXMWbMmDj99NNj4sSJsffee8fq1avjgw8+iDFjxsQzzzwTu+22W+yyyy5xwgknxC233BKLFy+OTp06xfPPP1/mN7BMmjQpFixYEEccccR65wAAwPptqXXGhtqQuuLaa6+NI444Ivbee+/o379/LFy4MG6++ebYaaedskKVXbp0idNOOy2uu+66mDx5cnTv3j2qVKkSH3/8cYwdOzZGjBiRyXWVV1n1QadOnaJevXrRt2/fOPfccyMvLy/uvffeEh+uK0vr1q1jp512iueeey5OOumkTHt5r3O0bds2rrjiihg6dGh07tw5fvGLX0RBQUH861//iqZNm8Z1110XEd9et7r11lvj6quvjrZt20bjxo1j//33j+7du0fz5s3j5JNPjt/85jdRqVKluOuuu6JRo0ZZAdTvus5+/frF3XffHTNmzMi6A23Tpk1j2LBhMXPmzPjJT34SDz74YEyePDlGjhy53tfchlwfK+/vU7EJEybE3nvvHQ0aNCjX+n5UEt/JSy+9lDp06JCqVq2aWrdunW677bY0cODAtPahffzxx9NPf/rTVFhYmFq2bJmGDRuW7rrrrhQRacaMGZl+LVq0SD179ix1XxGRzjrrrKy2xx57LFWuXDkdd9xxafXq1WXO8+uvv05nnXVWql+/fqpZs2Y68sgj04cffpgiIv3ud7/L9Cue+9y5c0uM8dlnn6Wjjjoq1a1bN9WpUycde+yx6fPPP08RkQYOHFii/4oVK1K9evVSnTp10rJly8qc29oeeeSRlJeXl/79739ntZf3GBb37dSpU6pWrVqqXbt22mOPPdJf//rXzONLlixJvXv3TnXr1k0RkVq0aJF5bPr06emAAw5IBQUFaauttkqXX355mjBhQoqINHHixEy/adOmpQMOOCDVrFkzNWzYMJ166qlpypQpKSLSqFGjShzTNV144YUpLy8vvf/++5m2vn37pho1aqTp06en7t27p+rVq6etttoqDRw4MOu5nTFjRoqIdMMNN5R6/KZPn5769OmTmjRpkqpUqZKaNWuWDj300PTQQw9l9XvnnXdSly5dUmFhYWrWrFkaOnRouvPOO0scz9WrV6ett946XXnllaXuDwCAipFLtUhKKT388MNpn332STVq1Eg1atRI7dq1S2eddVb68MMPs/q9/fbb6Re/+EVq0KBBKigoSC1atEi9evVKzz///Hr3NWrUqFLri9IcfvjhqVu3bllty5cvTxdeeGHaeuutU7Vq1dLee++dXn/99dSlS5fUpUuXrL5Lly5NV1xxRWrVqlWqUqVKatKkSTrmmGPS9OnTM31ee+21zHO8dt113333pdatW6eqVaumXXbZJT3zzDOpb9++WbVNSindeeedabvttksFBQWpXbt2adSoUaW+Tlq0aJH69u2b1dawYcPUsWPHrLbi18J9992XGXfXXXfNqpVS2nTP5y233JJatWqVCgoK0m677Zb+/ve/l3o8n3766RQR6eOPPy51fwAAfD+2lDqlPNtOnDgxRUQaO3ZsVr/i9+jXvA5Q3usFxdcB1jRv3ry04447piZNmmTOZ0s75y3NihUrUsOGDdPQoUOz2jfkes6sWbNSnz59UqNGjVJBQUFq3bp1Ouuss9KKFSsyff785z+n1q1bp0qVKmVdK1m9enW65JJLUsOGDVP16tVTjx490ieffFKivihvrVTasZ06dWqKiHTppZdm2oqfm7/+9a/psssuS40bN07VqlVLPXv2TLNmzcpaX5cuXVL79u1LPX4rV65Mw4YNS+3bt08FBQWpXr16qUOHDmnw4MFp8eLFmX7Lli1L5557bmrQoEGqUaNGOuyww9J//vOfUo/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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": 251,
+   "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_one_example(names[idx],colors[idx])\n",
+    "        plt.show()\n",
+    "\n",
+    "test_predict_show([\"red\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 254,
+   "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": 255,
+   "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": 262,
+   "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/AAAACXBIWXMAAA9hAAAPYQGoP6dpAAAKLElEQVR4nO3cT2hdZR7H4d9Ni6kdqkiNJUIEk1RMU+vCgNQuFA1okUJBEBeKolSqFHQh7sTShejGgn9rBReKCJVqcGFUChHUjYvWoKWFBEEhUtpQdVFjFrdnFkO/TCYddXQ66e08DwRy3vOenPdcLnw4OcltNU3TFABUVddSLwCA84coABCiAECIAgAhCgCEKAAQogBAiAIAIQoAhCgAEKIAQIgCACEK/N84ffp0/frrr0u9DDiviQId6dNPP62RkZFasWJFDQwM1GuvvVY7d+6sVquVOa1Wq3bs2FFvv/12DQ8PV3d3d3300UdVVTUzM1MPPvhgrVmzprq7u2t4eLjeeOONReeZn5+vp59+ugYHB6u7u7v6+vrqySefrPn5+QXzzpxrbGys1q9fn5955nzQKVo+OptOc+jQodq4cWP19vbW9u3bq91u18svv1w9PT01OTlZZ97SrVarhoaGanZ2tnbs2FGXX3553XTTTdXb21sjIyPVarVq27Zt1dPTU+Pj4/XBBx/U7t276/HHH6+qf9xZbN68uT7//PN6+OGHa2hoqL7++uvas2dP3XnnnTU2NpY1tVqtuv766+v48eP16KOP1qpVq+qFF16oY8eO1ffff1+rV69eglcK/oQGOsyWLVualStXNjMzMxmbmppqli9f3vzzW7qqmq6urubw4cMLjn/ooYea3t7eZnZ2dsH4Pffc01x66aXNL7/80jRN07z11ltNV1dX89lnny2Yt2fPnqaqmi+++GLBuS666KJmeno6Y5OTk01VNS+++OJfv2j4H/HrIzpKu92uAwcO1NatW+vKK6/M+ODgYG3evHnR/JtvvrnWrVuX7aZpav/+/bVly5ZqmqZmZ2fzdfvtt9fPP/9cBw8erKqqd999t4aGhuraa69dMO/WW2+tqqqJiYkF5xodHa2BgYFsb9iwoS655JL69ttv/6uvAZxLy5d6AfCfOH78eM3NzdXg4OCifWcbu/rqqxdsnzhxon766afau3dv7d2799+eo6pqamqqjhw5Uj09Pb8574yrrrpq0ZzLLrusfvzxx7NfDJyHRIEL2sUXX7xg+/Tp01VVde+999b9999/1mM2bNiQudddd109//zzZ53X19e3YHvZsmVnndd4bEcHEQU6yhVXXFErVqyo6enpRfvONvavenp6atWqVdVut2t0dPQ35w4MDNTk5GTddtttC/6qCS5kninQUZYtW1ajo6M1NjZWP/zwQ8anp6drfHz8Dx1/11131f79++ubb75ZtP/EiRP5/u67766ZmZl6/fXXF82bm5urU6dO/cmrgPOXOwU6zs6dO+uTTz6pTZs21SOPPFLtdrteeumlWr9+fX311Ve/e/yzzz5bExMTdeONN9a2bdtq3bp1dfLkyTp48GAdOHCgTp48WVVV9913X+3bt6+2b99eExMTtWnTpmq323X06NHat29fffzxxzUyMnKOrxb+t0SBjnPDDTfU+Ph4PfHEE/XUU09VX19f7dq1q44cOVJHjx793ePXrFlTX375Ze3atavee++9euWVV2r16tU1PDxczz33XOZ1dXXV2NhY7d69u9588816//33a+XKldXf31+PPfZYXXPNNefyMmFJ+Oc1Lhhbt26tw4cP19TU1FIvBTqWZwp0pLm5uQXbU1NT9eGHH9Ytt9yyNAuCC4Q7BTpSb29vPfDAA9Xf31/fffddvfrqqzU/P1+HDh2qtWvXLvXyoGN5pkBHuuOOO+qdd96pY8eOVXd3d23cuLGeeeYZQYC/yJ0CAOGZAgAhCgDEH36m8Lep/nO5DgDOsVNrf/8Te90pABCiAECIAgAhCgCEKAAQogBAiAIAIQoAhCgAEKIAQIgCACEKAIQoABCiAECIAgAhCgCEKAAQogBAiAIAIQoAhCgAEKIAQIgCACEKAIQoABCiAECIAgAhCgCEKAAQogBAiAIAIQoAhCgAEKIAQIgCACEKAIQoABCiAECIAgAhCgCEKAAQogBAiAIAIQoAhCgAEKIAQIgCACEKAIQoABCiAECIAgAhCgCEKAAQogBAiAIAIQoAhCgAEKIAQIgCACEKAIQoABCiAECIAgAhCgCEKAAQogBAiAIAIQoAhCgAEKIAQIgCACEKAIQoABCiAECIAgAhCgCEKAAQogBAiAIAIQoAhCgAEKIAQIgCACEKAIQoABCiAECIAgAhCgCEKAAQogBAiAIAIQoAhCgAEKIAQIgCACEKAIQoABCiAECIAgAhCgCEKAAQogBAiAIAIQoAhCgAEKIAQIgCACEKAIQoABCiAECIAgAhCgCEKAAQogBAiAIAIQoAhCgAEKIAQIgCACEKAIQoABCiAECIAgAhCgCEKAAQogBAiAIAIQoAhCgAEKIAQIgCACEKAIQoABCiAECIAgAhCgCEKAAQogBAiAIAIQoAhCgAEKIAQIgCACEKAIQoABCiAECIAgAhCgCEKAAQogBAiAIAIQoAhCgAEKIAQIgCACEKAIQoABCiAECIAgAhCgCEKAAQogBAiAIAIQoAhCgAEKIAQIgCACEKAIQoABCiAECIAgAhCgCEKAAQogBAiAIAIQoAhCgAEKIAQIgCACEKAIQoABCiAECIAgAhCgCEKAAQogBAiAIAIQoAhCgAEKIAQIgCACEKAIQoABCiAECIAgAhCgCEKAAQogBAiAIAIQoAhCgAEKIAQIgCACEKAIQoABCiAECIAgAhCgCEKAAQogBAiAIAIQoAhCgAEKIAQIgCACEKAIQoABCiAECIAgAhCgCEKAAQogBAiAIAIQoAhCgAEKIAQIgCACEKAIQoABCiAECIAgAhCgCEKAAQogBAiAIAIQoAhCgAEKIAQIgCACEKAIQoABCiAECIAgAhCgCEKAAQogBAiAIAIQoAhCgAEKIAQIgCACEKAIQoABCiAECIAgAhCgCEKAAQogBAiAIAIQoAhCgAEKIAQIgCACEKAIQoABCiAECIAgAhCgCEKAAQogBAiAIAIQoAhCgAEKIAQIgCACEKAIQoABCiAECIAgAhCgCEKAAQogBAiAIAIQoAhCgAEKIAQIgCACEKAIQoABCiAECIAgAhCgCEKAAQogBAiAIAIQoAhCgAEKIAQIgCACEKAIQoABCiAECIAgAhCgCEKAAQogBAiAIAIQoAhCgAEKIAQIgCACEKAIQoABCiAECIAgAhCgCEKAAQogBAiAIAIQoAhCgAEKIAQIgCACEKAIQoABCiAECIAgAhCgCEKAAQogBAiAIAIQoAhCgAEKIAQIgCACEKAIQoABCiAECIAgAhCgCEKAAQogBAiAIAIQoAhCgAEKIAQIgCACEKAIQoABCiAECIAgAhCgCEKAAQogBAiAIAIQoAhCgAEKIAQIgCACEKAIQoABCiAECIAgAhCgCEKAAQogBAiAIAIQoAhCgAEK2maZqlXgQA5wd3CgCEKAAQogBAiAIAIQoAhCgAEKIAQIgCACEKAMTfAXJlW2s1KrxGAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAGbCAYAAAAr/4yjAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8pXeV/AAAACXBIWXMAAA9hAAAPYQGoP6dpAAANQklEQVR4nO3caYhW5f/H8e8smZOppWlBlE2TbUIUUxG0r2Zl2DZlEVm0oRZBPYmKUWgvWjCyKDLCR0FlCS5ERRvRg0oKDLOyheiBSUy4EOic/4M/fmgal36S+fv/e71A8D5z3dd1HRHe5z7n1pamaZoCgKpq3dUbAOC/hygAEKIAQIgCACEKAIQoABCiAECIAgAhCgCEKPCPmzVrVrW0tPzt87a0tNTMmTP/9nnh30QUAAhRACBEgf/TmqapDRs27NI9rFu3bpeuD38nUWCn+uCDD+q4446roUOHVldXVz377LNbHDdv3rw644wzauzYsbX77rvXkUceWXPnzh007qCDDqoLLrigli5dWscee2x1dHRsdc6qqnvvvbdaW1trzpw529znhg0b6tZbb6199tmnhg8fXhdeeGH99NNP1dLSUrNmzcq4zc9Dli9fXldeeWXtvffeddJJJ+Xn8+fPr+7u7uro6KhRo0bVFVdcUT/++OOg9T7++OM699xza+TIkbXHHnvUqaeeWh9++OGAMZvX+vrrr2vatGm111571ciRI+vaa6+t9evXb/N8YEe17+oN8P/XF198Ueecc06NGTOmZs2aVRs3bqze3t7ad999B42dO3duTZgwoS688MJqb2+vhQsX1vTp06u/v79mzJgxYOyKFStq6tSpddNNN9UNN9xQhx122BbXv/vuu+v++++vZ599tm644YZt7nXatGn18ssv19VXX10nnHBCvfvuu3X++edvdfxll11W48ePr/vvv782/+/z9913X91zzz3V09NT119/fa1evbrmzJlTp5xySn322We11157VVXV22+/XZMmTaru7u7q7e2t1tbWRPH999+v448/fsBaPT091dnZWQ888EB9+umn9fzzz9fYsWProYce2uY5wQ5pYCeZMmVKM3To0Ob777/PseXLlzdtbW3Nn//qrV+/ftD7J06c2Bx88MEDjo0bN66pqmbJkiWDxldVM2PGjKZpmub2229vWltbmxdffHG7+/zkk0+aqmpuu+22AcenTZvWVFXT29ubY729vU1VNVOnTh0w9rvvvmva2tqa++67b8DxL774omlvb8/x/v7+Zvz48c3EiROb/v7+Aeff2dnZnH322YPWuu666wbMedFFFzWjR4/e7nnBjnD7iJ1i06ZNtXTp0poyZUodeOCBOX7EEUfUxIkTB43v6OjI7/v6+uqXX36pU089tb799tvq6+sbMLazs3OLc1T97zOGmTNn1pNPPlnz58+va665Zrt7XbJkSVVVTZ8+fcDxW265Zavvufnmmwe8fvXVV6u/v796enrql19+ya/99tuvxo8fX++8805VVS1btqxWrlxZV155Za1Zsybj1q1bV2eeeWa999571d/fv821Tj755FqzZk399ttv2z03+E+5fcROsXr16tqwYUONHz9+0M8OO+ywWrRo0YBjH374YfX29tZHH3006H55X19fjRw5Mq87Ozu3uu5LL71Ua9eurblz59bUqVP/0l6///77am1tHTTvIYccstX3/HnsypUrq2maLZ5vVdVuu+2WcVW1zVj19fXV3nvvndd/jGpV5We//vprjRgxYqvzwI4QBXa5b775ps4888w6/PDD67HHHqsDDjighgwZUosWLarHH3980JXzHz9V/NmJJ55Yy5Ytq6eeeqp6enpq1KhRO2XPf95Df39/tbS01OLFi6utrW3Q+D333DPjqqoeeeSROvroo7c49+axm21pvqrKswz4O4kCO8WYMWOqo6MjV8Z/tGLFigGvFy5cWL///nu98cYbA66KN99y+U8ccsgh9fDDD9dpp51W5557br311ls1fPjwbb5n3Lhx1d/fX6tWrRpwpf/111//5XW7urqqaZrq7OysQw89dJvjqqpGjBhRZ5111l+eH/4pnimwU7S1tdXEiRNrwYIF9cMPP+T4l19+WUuXLh00tmrglW9fX1/Nmzdvh9Y+6qijatGiRfXll1/W5MmTt/vvGDY/n3j66acHHN/e11j/6OKLL662traaPXv2oCv4pmlqzZo1VVXV3d1dXV1d9eijj9batWsHzbN69eq/vCbsDD4psNPMnj27lixZUieffHJNnz69Nm7cWHPmzKkJEybU559/nnHnnHNODRkypCZPnlw33XRTrV27tp577rkaO3Zs/fzzzzu09gknnFCvv/56nXfeeXXppZfWggULcl//z7q7u+uSSy6pJ554otasWZOvpH711VdVVX/p/2nq6uqqe++9t+6888767rvvasqUKTV8+PBatWpVvfbaa3XjjTfWHXfcUa2trfX888/XpEmTasKECXXttdfW/vvvXz/99FO98847NWLEiFq4cOEOnTP8LXbhN5/4F3j33Xeb7u7uZsiQIc3BBx/cPPPMM/mq5R+98cYbzVFHHdUMHTq0Oeigg5qHHnqoeeGFF5qqalatWpVx48aNa84///wtrlV/+ErqZq+//nrT3t7eXH755c2mTZu2us9169Y1M2bMaEaNGtXsueeezZQpU5oVK1Y0VdU8+OCDGbd576tXr97iPK+88kpz0kknNcOGDWuGDRvWHH744c2MGTOaFStWDBj32WefNRdffHEzevToZvfdd2/GjRvX9PT0NG+99dZ215o3b96gPxf4u7Q0jadVsCXLli2rY445pubPn19XXXXVrt4O/CM8U4CqLT53eOKJJ6q1tbVOOeWUXbAj2DU8U4Cqevjhh+uTTz6p008/vdrb22vx4sW1ePHiuvHGG+uAAw7Y1duDf4zbR1BVb775Zs2ePbuWL19ea9eurQMPPLCuvvrquuuuu6q93bUT/x6iAEB4pgBAiAIA8ZdvlnZM79qZ+wBgJ9vw9DfbHeOTAgAhCgCEKAAQogBAiAIAIQoAhCgAEKIAQIgCACEKAIQoABCiAECIAgAhCgCEKAAQogBAiAIAIQoAhCgAEKIAQIgCACEKAIQoABCiAECIAgAhCgCEKAAQogBAiAIAIQoAhCgAEKIAQIgCACEKAIQoABCiAECIAgAhCgCEKAAQogBAiAIAIQoAhCgAEKIAQIgCACEKAIQoABCiAECIAgAhCgCEKAAQogBAiAIAIQoAhCgAEKIAQIgCACEKAIQoABCiAECIAgAhCgCEKAAQogBAiAIAIQoAhCgAEKIAQIgCACEKAIQoABCiAECIAgAhCgCEKAAQogBAiAIAIQoAhCgAEKIAQIgCACEKAIQoABCiAECIAgAhCgCEKAAQogBAiAIAIQoAhCgAEKIAQIgCACEKAIQoABCiAECIAgAhCgCEKAAQogBAiAIAIQoAhCgAEKIAQIgCACEKAIQoABCiAECIAgAhCgCEKAAQogBAiAIAIQoAhCgAEKIAQIgCACEKAIQoABCiAECIAgAhCgCEKAAQogBAiAIAIQoAhCgAEKIAQIgCACEKAIQoABCiAECIAgAhCgCEKAAQogBAiAIAIQoAhCgAEKIAQIgCACEKAIQoABCiAECIAgAhCgCEKAAQogBAiAIAIQoAhCgAEKIAQIgCACEKAIQoABCiAECIAgAhCgCEKAAQogBAiAIAIQoAhCgAEKIAQIgCACEKAIQoABCiAECIAgAhCgCEKAAQogBAiAIAIQoAhCgAEKIAQIgCACEKAIQoABCiAECIAgAhCgCEKAAQogBAiAIAIQoAhCgAEKIAQIgCACEKAIQoABCiAECIAgAhCgCEKAAQogBAiAIAIQoAhCgAEKIAQIgCACEKAIQoABCiAECIAgAhCgCEKAAQogBAiAIAIQoAhCgAEKIAQIgCACEKAIQoABCiAECIAgAhCgCEKAAQogBAiAIAIQoAhCgAEKIAQIgCACEKAIQoABCiAECIAgAhCgCEKAAQogBAiAIAIQoAhCgAEKIAQIgCACEKAIQoABCiAECIAgAhCgCEKAAQogBAiAIAIQoAhCgAEKIAQIgCACEKAIQoABCiAECIAgAhCgCEKAAQogBAiAIAIQoAhCgAEKIAQIgCACEKAIQoABCiAECIAgAhCgCEKAAQogBAiAIAIQoAhCgAEKIAQIgCACEKAIQoABCiAECIAgAhCgCEKAAQogBAiAIAIQoAhCgAEKIAQIgCACEKAIQoABCiAECIAgAhCgCEKAAQogBAiAIAIQoAhCgAEKIAQIgCACEKAIQoABCiAECIAgAhCgCEKAAQogBAiAIAIQoAhCgAEKIAQIgCACEKAIQoABCiAECIAgAhCgCEKAAQogBAiAIAIQoAhCgAEKIAQIgCACEKAIQoABCiAECIAgAhCgCEKAAQogBAiAIAIQoAhCgAEKIAQIgCACEKAIQoABCiAECIAgAhCgCEKAAQogBAiAIAIQoAhCgAEKIAQIgCACEKAIQoABCiAECIAgAhCgCEKAAQogBAiAIAIQoAhCgAEKIAQIgCACEKAIQoABCiAECIAgAhCgCEKAAQogBAiAIAIQoAhCgAEKIAQIgCANHSNE2zqzcBwH8HnxQACFEAIEQBgBAFAEIUAAhRACBEAYAQBQBCFACI/wEUE3hH33OgmgAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAGbCAYAAAAr/4yjAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8pXeV/AAAACXBIWXMAAA9hAAAPYQGoP6dpAAANhElEQVR4nO3daYiV9d/H8e+MphmltjJQZmpmy4MWHxRRjm3aQmFF0z5ZtEML5YM2mrzbaFOjzbuCFmyjSFotIqMgokCQgqAsKyIqasgpM6Ka635w0+fuNC3qP5f7/3+9QPD8zu+6zu/nA9/Xuc4ZbWuapikAqKr2db0AANYfogBAiAIAIQoAhCgAEKIAQIgCACEKAIQoABCiwH+EyZMn1+TJk9f1MmC9JwoAhCgAEKLAWvH999+v6yWscf8Je+Tfnyjwj7vqqquqra2t3n333TrhhBNq0003rX322aeqqn7++ee6+uqra9y4cTV06NDabrvt6rLLLqsff/wxx59yyim1xRZb1E8//TTg3FOmTKkJEybk8X333Vf7779/bbXVVjV06NDaeeed66677lrttf/www91/vnn1xZbbFGbbLJJHXHEEfXZZ59VW1tbXXXVVSu1x6qqefPm1cSJE2vYsGG12Wab1XHHHVeffvrpgNd788036+CDD64RI0bURhttVJ2dnfX666//4Z/nBx98UNOnT6+RI0fWiBEj6tRTT60VK1as9l7hj4gCa8wxxxxTK1asqOuuu67OOOOMqqo6/fTT68orr6w99tijZs+eXZ2dnXX99dfXcccdl+NOPvnk6u3trRdffLHlfF988UUtXLiwTjrppIzdddddNXr06LrsssvqlltuqVGjRtW5555bd9xxx2qtefr06XXbbbfVoYceWjfccEMNGzasDjvssFXa47XXXlvd3d01fvz4mjVrVl144YX18ssv16RJk2rZsmU5duHChTVp0qT69ttvq6enp6677rpatmxZ7b///vXWW28NeK2urq767rvv6vrrr6+urq66//77a+bMmau1T/hTDfzDenp6mqpqjj/++JbxxYsXN1XVnH766S3jM2bMaKqqWbhwYdM0TfPLL78022yzTXPssce2zJs1a1bT1tbWLF26NGMrVqwY8PpTp05txo4d2zLW2dnZdHZ2/uW6Fy1a1FRVc+GFF7aMT58+vamqpqen52/3+PHHHzeDBg1qrr322pbxd955pxk8eHDG+/v7m/HjxzdTp05t+vv7W/YzZsyY5qCDDhrwWqeddlrLOY888shm8803/8s9waryToE15uyzz255/Pzzz1dV1UUXXdQyfvHFF1dV1XPPPVdVVe3t7XXiiSfW008/Xd99913mPfTQQ7X33nvXmDFjMjZs2LD8vq+vr77++uvq7OyspUuXVl9f3yqt94UXXqiqqnPPPbdl/LzzzvvTY36/xyeffLL6+/urq6urvv766/zq6Oio8ePH1yuvvFJVVYsXL64lS5bUCSecUL29vZn3/fff1wEHHFCvvfZa9ff3/+Vr7bvvvtXb21vffvvtKu0T/srgdb0A/n399i/vqqpPPvmk2tvba/vtt28Z7+joqJEjR9Ynn3ySse7u7rrhhhtq/vz51d3dXe+9914tWrSo5s6d23Ls66+/Xj09PfXGG28MuL/e19dXI0aMWOn1/rq+36/79+v9qz0uWbKkmqap8ePH/+H8DTbYIPOq/vfzkz/T19dXm266aR5vu+22Lc//+tw333xTw4cP/9PzwKoQBdaY317F/1ZbW9vfHrvzzjvXxIkTa968edXd3V3z5s2rIUOGVFdXV+Z8+OGHdcABB9SOO+5Ys2bNqlGjRtWQIUPq+eefr9mzZw+40l4Tfr/H/v7+amtrqwULFtSgQYMGzN94440zr6rqpptuqt122+0Pz/3r3F/90fmqqhr/oy7/IFFgrRk9enT19/fXkiVLaqeddsr4l19+WcuWLavRo0e3zO/u7q6LLrqoPv/883r44YfrsMMOa7lyfuaZZ+rHH3+sp59+uuUq+tdbNKu7vo8++qjlSv+DDz5Y6XOMGzeumqapMWPG1A477PCX86qqhg8fXgceeOBqrRfWBJ8psNYceuihVVU1Z86clvFZs2ZVVQ34ls/xxx9fbW1tdcEFF9TSpUtbvnVU9X9Xzr+9Uu7r66v77rtvtdY3derUqqq68847W8Zvu+22lT7HUUcdVYMGDaqZM2cOuIJvmqZ6e3urqmrixIk1bty4uvnmm2v58uUDzvPVV1+t6vLhH+GdAmvNrrvuWqecckrdfffdtWzZsurs7Ky33nqrHnjggZo2bVrtt99+LfO33HLLOvjgg+vxxx+vkSNHDojGlClTasiQIXX44YfXWWedVcuXL6977rmnttpqq/r8889XeX0TJ06so48+uubMmVO9vb2111571auvvlrvv/9+Va3cba9x48bVNddcU5deeml9/PHHNW3atNpkk03qo48+qvnz59eZZ55ZM2bMqPb29rr33nvrkEMOqV122aVOPfXU2nrrreuzzz6rV155pYYPH17PPPPMKu8B/lWiwFp177331tixY+v++++v+fPnV0dHR1166aXV09Pzh/O7u7vr2Wefra6urho6dGjLcxMmTKgnnniirrjiipoxY0Z1dHTUOeecU1tuuWWddtppq7W+Bx98sDo6OuqRRx6p+fPn14EHHliPPfZYTZgwoTbccMOVOscll1xSO+ywQ82ePTs/RzBq1KiaMmVKHXHEEZk3efLkeuONN+rqq6+u22+/vZYvX14dHR2155571llnnbVa64d/VVvjUyrWY0899VRNmzatXnvttdp3333XyRoWL15cu+++e82bN69OPPHEdbIGWFt8psB67Z577qmxY8e2/BMSa9IPP/wwYGzOnDnV3t5ekyZNWitrgHXJ7SPWS48++mi9/fbb9dxzz9Wtt966Uvfz/wk33nhjLVq0qPbbb78aPHhwLViwoBYsWFBnnnlmjRo1aq2sAdYlt49YL7W1tdXGG29cxx57bM2dO7cGD1471y8vvfRSzZw5s959991avnx5bbvttnXyySfX5ZdfvtbWAOuSKAAQPlMAIEQBgFjpm6Qd/+V70wD/n31x5X//7RzvFAAIUQAgRAGAEAUAQhQACFEAIEQBgBAFAEIUAAhRACBEAYAQBQBCFAAIUQAgRAGAEAUAQhQACFEAIEQBgBAFAEIUAAhRACBEAYAQBQBCFAAIUQAgRAGAEAUAQhQACFEAIEQBgBAFAEIUAAhRACBEAYAQBQBCFAAIUQAgRAGAEAUAQhQACFEAIEQBgBAFAEIUAAhRACBEAYAQBQBCFAAIUQAgRAGAEAUAQhQACFEAIEQBgBAFAEIUAAhRACBEAYAQBQBCFAAIUQAgRAGAEAUAQhQACFEAIEQBgBAFAEIUAAhRACBEAYAQBQBCFAAIUQAgRAGAEAUAQhQACFEAIEQBgBAFAEIUAAhRACBEAYAQBQBCFAAIUQAgRAGAEAUAQhQACFEAIEQBgBAFAEIUAAhRACBEAYAQBQBCFAAIUQAgRAGAEAUAQhQACFEAIEQBgBAFAEIUAAhRACBEAYAQBQBCFAAIUQAgRAGAEAUAQhQACFEAIEQBgBAFAEIUAAhRACBEAYAQBQBCFAAIUQAgRAGAEAUAQhQACFEAIEQBgBAFAEIUAAhRACBEAYAQBQBCFAAIUQAgRAGAEAUAQhQACFEAIEQBgBAFAEIUAAhRACBEAYAQBQBCFAAIUQAgRAGAEAUAQhQACFEAIEQBgBAFAEIUAAhRACBEAYAQBQBCFAAIUQAgRAGAEAUAQhQACFEAIEQBgBAFAEIUAAhRACBEAYAQBQBCFAAIUQAgRAGAEAUAQhQACFEAIEQBgBAFAEIUAAhRACBEAYAQBQBCFAAIUQAgRAGAEAUAQhQACFEAIEQBgBAFAEIUAAhRACBEAYAQBQBCFAAIUQAgRAGAEAUAQhQACFEAIEQBgBAFAEIUAAhRACBEAYAQBQBCFAAIUQAgRAGAEAUAQhQACFEAIEQBgBAFAEIUAAhRACBEAYAQBQBCFAAIUQAgRAGAEAUAQhQACFEAIEQBgBAFAEIUAAhRACBEAYAQBQBCFAAIUQAgRAGAEAUAQhQACFEAIEQBgBAFAEIUAAhRACBEAYAQBQBCFAAIUQAgRAGAEAUAQhQACFEAIEQBgBAFAEIUAAhRACBEAYAQBQBCFAAIUQAgRAGAEAUAQhQACFEAIEQBgBAFAEIUAAhRACBEAYAQBQBCFAAIUQAgRAGAEAUAQhQACFEAIEQBgBAFAEIUAAhRACBEAYAQBQBCFAAIUQAgRAGAEAUAQhQACFEAIEQBgBAFAEIUAAhRACBEAYAQBQBCFAAIUQAgRAGAEAUAQhQACFEAIEQBgBAFAEIUAAhRACBEAYAQBQBCFAAIUQAgRAGAEAUAQhQACFEAIEQBgBAFAEIUAAhRACBEAYAQBQBCFAAIUQAgRAGAEAUAQhQACFEAIEQBgBAFAEIUAAhRACBEAYAQBQBCFAAIUQAgRAGAEAUAQhQACFEAIEQBgBAFAEIUAAhRACBEAYAQBQBCFAAIUQAgRAGAEAUAQhQACFEAIEQBgBAFAEIUAAhRACBEAYAQBQBCFAAIUQAgRAGAEAUAQhQACFEAIEQBgBAFAEIUAAhRACBEAYAQBQBCFAAIUQAgRAGAEAUAQhQACFEAIEQBgBAFAEIUAIi2pmmadb0IANYP3ikAEKIAQIgCACEKAIQoABCiAECIAgAhCgCEKAAQ/wPJiBoHP9rs9QAAAABJRU5ErkJggg==",
+      "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": "markdown",
+   "id": "5e95c580",
+   "metadata": {},
+   "source": [
+    "## Exporting the model and token_dict"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 275,
+   "id": "a96b4b14",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "save_dir = \"models/colgen1/\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 277,
+   "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": []
+  }
+ ],
+ "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": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}