real_data_train_keyboard.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.models import Sequential, load_model\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "from tensorflow.keras.layers import InputLayer, Dense, BatchNormalization\n",
    "from tensorflow.keras.utils import plot_model\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "gpus = tf.config.experimental.list_physical_devices('GPU')\n",
    "if gpus:\n",
    "    try:\n",
    "        for gpu in gpus:\n",
    "            tf.config.experimental.set_memory_growth(gpu, True)\n",
    "    except RuntimeError as e:\n",
    "        print(e)\n",
    "        \n",
    "np.random.seed(seed=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. 실제 수집한 데이터 불러오기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 버튼&배경 색상표\n",
    "colors = [[142, 10, 106], [129, 251, 185], [128, 232, 19], [164, 105, 114], [235, 89, 162], [157, 101, 32], [16, 219, 30]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 각 버튼 별 좌표 데이터 가져와서 하나의 데이터 프레임으로 합치는 작업\n",
    "df = pd.DataFrame(columns=['x', 'y', 'label'])\n",
    "\n",
    "for i, data in enumerate(os.listdir('hand_extract_data')):\n",
    "    temp = pd.read_csv('hand_extract_data/'+data).astype('int')\n",
    "    temp['label'] = i\n",
    "    df = df.append(temp, ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. 데이터 전처리"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.698801</td>\n",
       "      <td>0.101019</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>0</td>\n",
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       "      <th>2</th>\n",
       "      <td>0.732152</td>\n",
       "      <td>0.481928</td>\n",
       "      <td>0</td>\n",
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       "      <th>3</th>\n",
       "      <td>0.089109</td>\n",
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       "      <td>0</td>\n",
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       "      <th>4</th>\n",
       "      <td>0.197499</td>\n",
       "      <td>0.082484</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          x         y label\n",
       "0  0.698801  0.101019     0\n",
       "1  0.904638  0.943466     0\n",
       "2  0.732152  0.481928     0\n",
       "3  0.089109  0.457831     0\n",
       "4  0.197499  0.082484     0"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 데이터 0~1로 정규화 작업\n",
    "df['x'] = (df['x'] - df['x'].min()) / (df['x'].max() - df['x'].min())\n",
    "df['y'] = (df['y'] - df['y'].min()) / (df['y'].max() - df['y'].min())\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_data = df.drop(columns=['label']).values\n",
    "y_data = df['label'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 학습 데이터 전처리\n",
    "X_train, X_val, y_train, y_val = train_test_split(X_data, y_data, test_size=0.2, random_state=42)\n",
    "\n",
    "X_train = tf.convert_to_tensor(X_train, dtype=tf.float64)\n",
    "X_val = tf.convert_to_tensor(X_val, dtype=tf.float64)\n",
    "\n",
    "y_train = tf.convert_to_tensor(y_train, dtype=tf.float64)\n",
    "y_val = tf.convert_to_tensor(y_val, dtype=tf.float64)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. 학습 모델 생성"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense (Dense)                (None, 32)                96        \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 64)                2112      \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 32)                2080      \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 7)                 231       \n",
      "=================================================================\n",
      "Total params: 4,519\n",
      "Trainable params: 4,519\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = Sequential([\n",
    "    InputLayer(input_shape=(2,)),\n",
    "    Dense(32, activation='relu'),\n",
    "    Dense(64, activation='relu'),\n",
    "    Dense(32, activation='relu'),\n",
    "    Dense(7, activation='softmax')\n",
    "])\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. 모델 학습"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer=Adam(learning_rate=0.001), loss='sparse_categorical_crossentropy', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:`period` argument is deprecated. Please use `save_freq` to specify the frequency in number of batches seen.\n"
     ]
    }
   ],
   "source": [
    "ckpt_dir = 'ckpt'\n",
    "ckpt_path = ckpt_dir+'/keyboard_{epoch}.ckpt'\n",
    "\n",
    "period = 1\n",
    "\n",
    "ckpt_callback = tf.keras.callbacks.ModelCheckpoint(filepath=ckpt_path, save_weights_only=True, verbose=1, period=period)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/500\n",
      "  1/183 [..............................] - ETA: 1s - loss: 0.1971 - accuracy: 0.9609"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "183/183 [==============================] - 1s 5ms/step - loss: 0.2565 - accuracy: 0.9057 - val_loss: 0.2453 - val_accuracy: 0.9143\n",
      "\n",
      "Epoch 00001: saving model to ckpt\\keyboard_1.ckpt\n",
      "Epoch 2/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.2436 - accuracy: 0.9124 - val_loss: 0.2369 - val_accuracy: 0.9211\n",
      "\n",
      "Epoch 00002: saving model to ckpt\\keyboard_2.ckpt\n",
      "Epoch 3/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.2368 - accuracy: 0.9161 - val_loss: 0.2341 - val_accuracy: 0.9199\n",
      "\n",
      "Epoch 00003: saving model to ckpt\\keyboard_3.ckpt\n",
      "Epoch 4/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.2287 - accuracy: 0.9174 - val_loss: 0.2228 - val_accuracy: 0.9261\n",
      "\n",
      "Epoch 00004: saving model to ckpt\\keyboard_4.ckpt\n",
      "Epoch 5/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.2225 - accuracy: 0.9214 - val_loss: 0.2139 - val_accuracy: 0.9259\n",
      "\n",
      "Epoch 00005: saving model to ckpt\\keyboard_5.ckpt\n",
      "Epoch 6/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.2158 - accuracy: 0.9240 - val_loss: 0.2133 - val_accuracy: 0.9294\n",
      "\n",
      "Epoch 00006: saving model to ckpt\\keyboard_6.ckpt\n",
      "Epoch 7/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.2136 - accuracy: 0.9250 - val_loss: 0.2080 - val_accuracy: 0.9340\n",
      "\n",
      "Epoch 00007: saving model to ckpt\\keyboard_7.ckpt\n",
      "Epoch 8/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.2091 - accuracy: 0.9263 - val_loss: 0.2123 - val_accuracy: 0.9263\n",
      "\n",
      "Epoch 00008: saving model to ckpt\\keyboard_8.ckpt\n",
      "Epoch 9/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.2045 - accuracy: 0.9276 - val_loss: 0.2010 - val_accuracy: 0.9378\n",
      "\n",
      "Epoch 00009: saving model to ckpt\\keyboard_9.ckpt\n",
      "Epoch 10/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.2020 - accuracy: 0.9288 - val_loss: 0.1952 - val_accuracy: 0.9338\n",
      "\n",
      "Epoch 00010: saving model to ckpt\\keyboard_10.ckpt\n",
      "Epoch 11/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1984 - accuracy: 0.9289 - val_loss: 0.1996 - val_accuracy: 0.9266\n",
      "\n",
      "Epoch 00011: saving model to ckpt\\keyboard_11.ckpt\n",
      "Epoch 12/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1952 - accuracy: 0.9312 - val_loss: 0.1889 - val_accuracy: 0.9400\n",
      "\n",
      "Epoch 00012: saving model to ckpt\\keyboard_12.ckpt\n",
      "Epoch 13/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1925 - accuracy: 0.9322 - val_loss: 0.1992 - val_accuracy: 0.9280\n",
      "\n",
      "Epoch 00013: saving model to ckpt\\keyboard_13.ckpt\n",
      "Epoch 14/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1944 - accuracy: 0.9310 - val_loss: 0.1943 - val_accuracy: 0.9374\n",
      "\n",
      "Epoch 00014: saving model to ckpt\\keyboard_14.ckpt\n",
      "Epoch 15/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1899 - accuracy: 0.9339 - val_loss: 0.1883 - val_accuracy: 0.9379\n",
      "\n",
      "Epoch 00015: saving model to ckpt\\keyboard_15.ckpt\n",
      "Epoch 16/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1895 - accuracy: 0.9333 - val_loss: 0.1858 - val_accuracy: 0.9391\n",
      "\n",
      "Epoch 00016: saving model to ckpt\\keyboard_16.ckpt\n",
      "Epoch 17/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1882 - accuracy: 0.9332 - val_loss: 0.1819 - val_accuracy: 0.9376\n",
      "\n",
      "Epoch 00017: saving model to ckpt\\keyboard_17.ckpt\n",
      "Epoch 18/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1871 - accuracy: 0.9345 - val_loss: 0.1846 - val_accuracy: 0.9366\n",
      "\n",
      "Epoch 00018: saving model to ckpt\\keyboard_18.ckpt\n",
      "Epoch 19/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1857 - accuracy: 0.9326 - val_loss: 0.1841 - val_accuracy: 0.9354\n",
      "\n",
      "Epoch 00019: saving model to ckpt\\keyboard_19.ckpt\n",
      "Epoch 20/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1878 - accuracy: 0.9325 - val_loss: 0.1781 - val_accuracy: 0.9395\n",
      "\n",
      "Epoch 00020: saving model to ckpt\\keyboard_20.ckpt\n",
      "Epoch 21/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1822 - accuracy: 0.9352 - val_loss: 0.1785 - val_accuracy: 0.9427\n",
      "\n",
      "Epoch 00021: saving model to ckpt\\keyboard_21.ckpt\n",
      "Epoch 22/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1830 - accuracy: 0.9364 - val_loss: 0.1756 - val_accuracy: 0.9403\n",
      "\n",
      "Epoch 00022: saving model to ckpt\\keyboard_22.ckpt\n",
      "Epoch 23/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1797 - accuracy: 0.9359 - val_loss: 0.1784 - val_accuracy: 0.9410\n",
      "\n",
      "Epoch 00023: saving model to ckpt\\keyboard_23.ckpt\n",
      "Epoch 24/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1802 - accuracy: 0.9360 - val_loss: 0.1736 - val_accuracy: 0.9403\n",
      "\n",
      "Epoch 00024: saving model to ckpt\\keyboard_24.ckpt\n",
      "Epoch 25/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1792 - accuracy: 0.9376 - val_loss: 0.1776 - val_accuracy: 0.9398\n",
      "\n",
      "Epoch 00025: saving model to ckpt\\keyboard_25.ckpt\n",
      "Epoch 26/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1787 - accuracy: 0.9376 - val_loss: 0.1766 - val_accuracy: 0.9390\n",
      "\n",
      "Epoch 00026: saving model to ckpt\\keyboard_26.ckpt\n",
      "Epoch 27/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1774 - accuracy: 0.9378 - val_loss: 0.1767 - val_accuracy: 0.9402\n",
      "\n",
      "Epoch 00027: saving model to ckpt\\keyboard_27.ckpt\n",
      "Epoch 28/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1773 - accuracy: 0.9370 - val_loss: 0.1731 - val_accuracy: 0.9407\n",
      "\n",
      "Epoch 00028: saving model to ckpt\\keyboard_28.ckpt\n",
      "Epoch 29/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1765 - accuracy: 0.9377 - val_loss: 0.1752 - val_accuracy: 0.9414\n",
      "\n",
      "Epoch 00029: saving model to ckpt\\keyboard_29.ckpt\n",
      "Epoch 30/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1779 - accuracy: 0.9376 - val_loss: 0.1721 - val_accuracy: 0.9434\n",
      "\n",
      "Epoch 00030: saving model to ckpt\\keyboard_30.ckpt\n",
      "Epoch 31/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1771 - accuracy: 0.9380 - val_loss: 0.1768 - val_accuracy: 0.9393\n",
      "\n",
      "Epoch 00031: saving model to ckpt\\keyboard_31.ckpt\n",
      "Epoch 32/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1773 - accuracy: 0.9364 - val_loss: 0.1741 - val_accuracy: 0.9429\n",
      "\n",
      "Epoch 00032: saving model to ckpt\\keyboard_32.ckpt\n",
      "Epoch 33/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1756 - accuracy: 0.9384 - val_loss: 0.1792 - val_accuracy: 0.9369\n",
      "\n",
      "Epoch 00033: saving model to ckpt\\keyboard_33.ckpt\n",
      "Epoch 34/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1747 - accuracy: 0.9391 - val_loss: 0.1782 - val_accuracy: 0.9405\n",
      "\n",
      "Epoch 00034: saving model to ckpt\\keyboard_34.ckpt\n",
      "Epoch 35/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1777 - accuracy: 0.9369 - val_loss: 0.1718 - val_accuracy: 0.9448\n",
      "\n",
      "Epoch 00035: saving model to ckpt\\keyboard_35.ckpt\n",
      "Epoch 36/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1726 - accuracy: 0.9387 - val_loss: 0.1791 - val_accuracy: 0.9390\n",
      "\n",
      "Epoch 00036: saving model to ckpt\\keyboard_36.ckpt\n",
      "Epoch 37/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1736 - accuracy: 0.9391 - val_loss: 0.1710 - val_accuracy: 0.9427\n",
      "\n",
      "Epoch 00037: saving model to ckpt\\keyboard_37.ckpt\n",
      "Epoch 38/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1755 - accuracy: 0.9376 - val_loss: 0.1728 - val_accuracy: 0.9415\n",
      "\n",
      "Epoch 00038: saving model to ckpt\\keyboard_38.ckpt\n",
      "Epoch 39/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1726 - accuracy: 0.9396 - val_loss: 0.1693 - val_accuracy: 0.9458\n",
      "\n",
      "Epoch 00039: saving model to ckpt\\keyboard_39.ckpt\n",
      "Epoch 40/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1729 - accuracy: 0.9389 - val_loss: 0.1670 - val_accuracy: 0.9451\n",
      "\n",
      "Epoch 00040: saving model to ckpt\\keyboard_40.ckpt\n",
      "Epoch 41/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1754 - accuracy: 0.9392 - val_loss: 0.1714 - val_accuracy: 0.9415\n",
      "\n",
      "Epoch 00041: saving model to ckpt\\keyboard_41.ckpt\n",
      "Epoch 42/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1718 - accuracy: 0.9397 - val_loss: 0.1799 - val_accuracy: 0.9369\n",
      "\n",
      "Epoch 00042: saving model to ckpt\\keyboard_42.ckpt\n",
      "Epoch 43/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1730 - accuracy: 0.9395 - val_loss: 0.1688 - val_accuracy: 0.9434\n",
      "\n",
      "Epoch 00043: saving model to ckpt\\keyboard_43.ckpt\n",
      "Epoch 44/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1719 - accuracy: 0.9404 - val_loss: 0.1702 - val_accuracy: 0.9395\n",
      "\n",
      "Epoch 00044: saving model to ckpt\\keyboard_44.ckpt\n",
      "Epoch 45/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1699 - accuracy: 0.9410 - val_loss: 0.1734 - val_accuracy: 0.9422\n",
      "\n",
      "Epoch 00045: saving model to ckpt\\keyboard_45.ckpt\n",
      "Epoch 46/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1703 - accuracy: 0.9409 - val_loss: 0.1677 - val_accuracy: 0.9421\n",
      "\n",
      "Epoch 00046: saving model to ckpt\\keyboard_46.ckpt\n",
      "Epoch 47/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1736 - accuracy: 0.9397 - val_loss: 0.1627 - val_accuracy: 0.9457\n",
      "\n",
      "Epoch 00047: saving model to ckpt\\keyboard_47.ckpt\n",
      "Epoch 48/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1703 - accuracy: 0.9410 - val_loss: 0.1700 - val_accuracy: 0.9479\n",
      "\n",
      "Epoch 00048: saving model to ckpt\\keyboard_48.ckpt\n",
      "Epoch 49/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1703 - accuracy: 0.9411 - val_loss: 0.1802 - val_accuracy: 0.9361\n",
      "\n",
      "Epoch 00049: saving model to ckpt\\keyboard_49.ckpt\n",
      "Epoch 50/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1725 - accuracy: 0.9391 - val_loss: 0.1686 - val_accuracy: 0.9410\n",
      "\n",
      "Epoch 00050: saving model to ckpt\\keyboard_50.ckpt\n",
      "Epoch 51/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1700 - accuracy: 0.9411 - val_loss: 0.1663 - val_accuracy: 0.9453\n",
      "\n",
      "Epoch 00051: saving model to ckpt\\keyboard_51.ckpt\n",
      "Epoch 52/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1695 - accuracy: 0.9407 - val_loss: 0.1638 - val_accuracy: 0.9453\n",
      "\n",
      "Epoch 00052: saving model to ckpt\\keyboard_52.ckpt\n",
      "Epoch 53/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1682 - accuracy: 0.9411 - val_loss: 0.1656 - val_accuracy: 0.9451\n",
      "\n",
      "Epoch 00053: saving model to ckpt\\keyboard_53.ckpt\n",
      "Epoch 54/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1706 - accuracy: 0.9412 - val_loss: 0.1801 - val_accuracy: 0.9367\n",
      "\n",
      "Epoch 00054: saving model to ckpt\\keyboard_54.ckpt\n",
      "Epoch 55/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1680 - accuracy: 0.9409 - val_loss: 0.1641 - val_accuracy: 0.9446\n",
      "\n",
      "Epoch 00055: saving model to ckpt\\keyboard_55.ckpt\n",
      "Epoch 56/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1690 - accuracy: 0.9408 - val_loss: 0.1649 - val_accuracy: 0.9453\n",
      "\n",
      "Epoch 00056: saving model to ckpt\\keyboard_56.ckpt\n",
      "Epoch 57/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1708 - accuracy: 0.9405 - val_loss: 0.1641 - val_accuracy: 0.9458\n",
      "\n",
      "Epoch 00057: saving model to ckpt\\keyboard_57.ckpt\n",
      "Epoch 58/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1672 - accuracy: 0.9425 - val_loss: 0.1694 - val_accuracy: 0.9429\n",
      "\n",
      "Epoch 00058: saving model to ckpt\\keyboard_58.ckpt\n",
      "Epoch 59/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1692 - accuracy: 0.9410 - val_loss: 0.1754 - val_accuracy: 0.9386\n",
      "\n",
      "Epoch 00059: saving model to ckpt\\keyboard_59.ckpt\n",
      "Epoch 60/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1694 - accuracy: 0.9408 - val_loss: 0.1631 - val_accuracy: 0.9463\n",
      "\n",
      "Epoch 00060: saving model to ckpt\\keyboard_60.ckpt\n",
      "Epoch 61/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1686 - accuracy: 0.9427 - val_loss: 0.1647 - val_accuracy: 0.9448\n",
      "\n",
      "Epoch 00061: saving model to ckpt\\keyboard_61.ckpt\n",
      "Epoch 62/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1658 - accuracy: 0.9419 - val_loss: 0.1611 - val_accuracy: 0.9477\n",
      "\n",
      "Epoch 00062: saving model to ckpt\\keyboard_62.ckpt\n",
      "Epoch 63/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1681 - accuracy: 0.9405 - val_loss: 0.1604 - val_accuracy: 0.9446\n",
      "\n",
      "Epoch 00063: saving model to ckpt\\keyboard_63.ckpt\n",
      "Epoch 64/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1670 - accuracy: 0.9417 - val_loss: 0.1675 - val_accuracy: 0.9431\n",
      "\n",
      "Epoch 00064: saving model to ckpt\\keyboard_64.ckpt\n",
      "Epoch 65/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1679 - accuracy: 0.9423 - val_loss: 0.1702 - val_accuracy: 0.9424\n",
      "\n",
      "Epoch 00065: saving model to ckpt\\keyboard_65.ckpt\n",
      "Epoch 66/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1682 - accuracy: 0.9418 - val_loss: 0.1732 - val_accuracy: 0.9395\n",
      "\n",
      "Epoch 00066: saving model to ckpt\\keyboard_66.ckpt\n",
      "Epoch 67/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1671 - accuracy: 0.9414 - val_loss: 0.1701 - val_accuracy: 0.9429\n",
      "\n",
      "Epoch 00067: saving model to ckpt\\keyboard_67.ckpt\n",
      "Epoch 68/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1670 - accuracy: 0.9418 - val_loss: 0.1675 - val_accuracy: 0.9439\n",
      "\n",
      "Epoch 00068: saving model to ckpt\\keyboard_68.ckpt\n",
      "Epoch 69/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1667 - accuracy: 0.9412 - val_loss: 0.1566 - val_accuracy: 0.9481\n",
      "\n",
      "Epoch 00069: saving model to ckpt\\keyboard_69.ckpt\n",
      "Epoch 70/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1667 - accuracy: 0.9423 - val_loss: 0.1675 - val_accuracy: 0.9438\n",
      "\n",
      "Epoch 00070: saving model to ckpt\\keyboard_70.ckpt\n",
      "Epoch 71/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1668 - accuracy: 0.9412 - val_loss: 0.1681 - val_accuracy: 0.9448\n",
      "\n",
      "Epoch 00071: saving model to ckpt\\keyboard_71.ckpt\n",
      "Epoch 72/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1667 - accuracy: 0.9417 - val_loss: 0.1627 - val_accuracy: 0.9451\n",
      "\n",
      "Epoch 00072: saving model to ckpt\\keyboard_72.ckpt\n",
      "Epoch 73/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1654 - accuracy: 0.9428 - val_loss: 0.1651 - val_accuracy: 0.9446\n",
      "\n",
      "Epoch 00073: saving model to ckpt\\keyboard_73.ckpt\n",
      "Epoch 74/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1669 - accuracy: 0.9414 - val_loss: 0.1744 - val_accuracy: 0.9369\n",
      "\n",
      "Epoch 00074: saving model to ckpt\\keyboard_74.ckpt\n",
      "Epoch 75/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1650 - accuracy: 0.9416 - val_loss: 0.1601 - val_accuracy: 0.9472\n",
      "\n",
      "Epoch 00075: saving model to ckpt\\keyboard_75.ckpt\n",
      "Epoch 76/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1664 - accuracy: 0.9416 - val_loss: 0.1752 - val_accuracy: 0.9367\n",
      "\n",
      "Epoch 00076: saving model to ckpt\\keyboard_76.ckpt\n",
      "Epoch 77/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1660 - accuracy: 0.9421 - val_loss: 0.1653 - val_accuracy: 0.9427\n",
      "\n",
      "Epoch 00077: saving model to ckpt\\keyboard_77.ckpt\n",
      "Epoch 78/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1663 - accuracy: 0.9425 - val_loss: 0.1681 - val_accuracy: 0.9415\n",
      "\n",
      "Epoch 00078: saving model to ckpt\\keyboard_78.ckpt\n",
      "Epoch 79/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1651 - accuracy: 0.9433 - val_loss: 0.1668 - val_accuracy: 0.9457\n",
      "\n",
      "Epoch 00079: saving model to ckpt\\keyboard_79.ckpt\n",
      "Epoch 80/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1639 - accuracy: 0.9448 - val_loss: 0.1567 - val_accuracy: 0.9493\n",
      "\n",
      "Epoch 00080: saving model to ckpt\\keyboard_80.ckpt\n",
      "Epoch 81/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1637 - accuracy: 0.9427 - val_loss: 0.1578 - val_accuracy: 0.9499\n",
      "\n",
      "Epoch 00081: saving model to ckpt\\keyboard_81.ckpt\n",
      "Epoch 82/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1627 - accuracy: 0.9436 - val_loss: 0.1611 - val_accuracy: 0.9455\n",
      "\n",
      "Epoch 00082: saving model to ckpt\\keyboard_82.ckpt\n",
      "Epoch 83/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1645 - accuracy: 0.9436 - val_loss: 0.1596 - val_accuracy: 0.9481\n",
      "\n",
      "Epoch 00083: saving model to ckpt\\keyboard_83.ckpt\n",
      "Epoch 84/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1645 - accuracy: 0.9430 - val_loss: 0.1575 - val_accuracy: 0.9496\n",
      "\n",
      "Epoch 00084: saving model to ckpt\\keyboard_84.ckpt\n",
      "Epoch 85/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1642 - accuracy: 0.9426 - val_loss: 0.1712 - val_accuracy: 0.9431\n",
      "\n",
      "Epoch 00085: saving model to ckpt\\keyboard_85.ckpt\n",
      "Epoch 86/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1653 - accuracy: 0.9424 - val_loss: 0.1576 - val_accuracy: 0.9470\n",
      "\n",
      "Epoch 00086: saving model to ckpt\\keyboard_86.ckpt\n",
      "Epoch 87/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1653 - accuracy: 0.9422 - val_loss: 0.1610 - val_accuracy: 0.9496\n",
      "\n",
      "Epoch 00087: saving model to ckpt\\keyboard_87.ckpt\n",
      "Epoch 88/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1655 - accuracy: 0.9418 - val_loss: 0.1684 - val_accuracy: 0.9431\n",
      "\n",
      "Epoch 00088: saving model to ckpt\\keyboard_88.ckpt\n",
      "Epoch 89/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1645 - accuracy: 0.9432 - val_loss: 0.1641 - val_accuracy: 0.9443\n",
      "\n",
      "Epoch 00089: saving model to ckpt\\keyboard_89.ckpt\n",
      "Epoch 90/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1645 - accuracy: 0.9434 - val_loss: 0.1568 - val_accuracy: 0.9484\n",
      "\n",
      "Epoch 00090: saving model to ckpt\\keyboard_90.ckpt\n",
      "Epoch 91/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1618 - accuracy: 0.9446 - val_loss: 0.1669 - val_accuracy: 0.9438\n",
      "\n",
      "Epoch 00091: saving model to ckpt\\keyboard_91.ckpt\n",
      "Epoch 92/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1636 - accuracy: 0.9430 - val_loss: 0.1558 - val_accuracy: 0.9487\n",
      "\n",
      "Epoch 00092: saving model to ckpt\\keyboard_92.ckpt\n",
      "Epoch 93/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1632 - accuracy: 0.9440 - val_loss: 0.1619 - val_accuracy: 0.9479\n",
      "\n",
      "Epoch 00093: saving model to ckpt\\keyboard_93.ckpt\n",
      "Epoch 94/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1647 - accuracy: 0.9418 - val_loss: 0.1656 - val_accuracy: 0.9445\n",
      "\n",
      "Epoch 00094: saving model to ckpt\\keyboard_94.ckpt\n",
      "Epoch 95/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1623 - accuracy: 0.9439 - val_loss: 0.1588 - val_accuracy: 0.9484\n",
      "\n",
      "Epoch 00095: saving model to ckpt\\keyboard_95.ckpt\n",
      "Epoch 96/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1662 - accuracy: 0.9412 - val_loss: 0.1687 - val_accuracy: 0.9415\n",
      "\n",
      "Epoch 00096: saving model to ckpt\\keyboard_96.ckpt\n",
      "Epoch 97/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1622 - accuracy: 0.9437 - val_loss: 0.1608 - val_accuracy: 0.9441\n",
      "\n",
      "Epoch 00097: saving model to ckpt\\keyboard_97.ckpt\n",
      "Epoch 98/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1623 - accuracy: 0.9437 - val_loss: 0.1561 - val_accuracy: 0.9486\n",
      "\n",
      "Epoch 00098: saving model to ckpt\\keyboard_98.ckpt\n",
      "Epoch 99/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1635 - accuracy: 0.9448 - val_loss: 0.1715 - val_accuracy: 0.9409\n",
      "\n",
      "Epoch 00099: saving model to ckpt\\keyboard_99.ckpt\n",
      "Epoch 100/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1629 - accuracy: 0.9433 - val_loss: 0.1610 - val_accuracy: 0.9458\n",
      "\n",
      "Epoch 00100: saving model to ckpt\\keyboard_100.ckpt\n",
      "Epoch 101/500\n",
      "183/183 [==============================] - 1s 3ms/step - loss: 0.1639 - accuracy: 0.9441 - val_loss: 0.1630 - val_accuracy: 0.9474\n",
      "\n",
      "Epoch 00101: saving model to ckpt\\keyboard_101.ckpt\n",
      "Epoch 102/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1633 - accuracy: 0.9423 - val_loss: 0.1638 - val_accuracy: 0.9426\n",
      "\n",
      "Epoch 00102: saving model to ckpt\\keyboard_102.ckpt\n",
      "Epoch 103/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1628 - accuracy: 0.9436 - val_loss: 0.1592 - val_accuracy: 0.9462\n",
      "\n",
      "Epoch 00103: saving model to ckpt\\keyboard_103.ckpt\n",
      "Epoch 104/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1656 - accuracy: 0.9421 - val_loss: 0.1591 - val_accuracy: 0.9469\n",
      "\n",
      "Epoch 00104: saving model to ckpt\\keyboard_104.ckpt\n",
      "Epoch 105/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1625 - accuracy: 0.9446 - val_loss: 0.1560 - val_accuracy: 0.9513\n",
      "\n",
      "Epoch 00105: saving model to ckpt\\keyboard_105.ckpt\n",
      "Epoch 106/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1626 - accuracy: 0.9439 - val_loss: 0.1575 - val_accuracy: 0.9465\n",
      "\n",
      "Epoch 00106: saving model to ckpt\\keyboard_106.ckpt\n",
      "Epoch 107/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1617 - accuracy: 0.9454 - val_loss: 0.1621 - val_accuracy: 0.9469\n",
      "\n",
      "Epoch 00107: saving model to ckpt\\keyboard_107.ckpt\n",
      "Epoch 108/500\n",
      "183/183 [==============================] - 1s 3ms/step - loss: 0.1616 - accuracy: 0.9447 - val_loss: 0.1577 - val_accuracy: 0.9503\n",
      "\n",
      "Epoch 00108: saving model to ckpt\\keyboard_108.ckpt\n",
      "Epoch 109/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1631 - accuracy: 0.9450 - val_loss: 0.1660 - val_accuracy: 0.9426\n",
      "\n",
      "Epoch 00109: saving model to ckpt\\keyboard_109.ckpt\n",
      "Epoch 110/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1639 - accuracy: 0.9434 - val_loss: 0.1583 - val_accuracy: 0.9484\n",
      "\n",
      "Epoch 00110: saving model to ckpt\\keyboard_110.ckpt\n",
      "Epoch 111/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1611 - accuracy: 0.9444 - val_loss: 0.1570 - val_accuracy: 0.9491\n",
      "\n",
      "Epoch 00111: saving model to ckpt\\keyboard_111.ckpt\n",
      "Epoch 112/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1642 - accuracy: 0.9433 - val_loss: 0.1560 - val_accuracy: 0.9481\n",
      "\n",
      "Epoch 00112: saving model to ckpt\\keyboard_112.ckpt\n",
      "Epoch 113/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1623 - accuracy: 0.9432 - val_loss: 0.1584 - val_accuracy: 0.9457\n",
      "\n",
      "Epoch 00113: saving model to ckpt\\keyboard_113.ckpt\n",
      "Epoch 114/500\n",
      "183/183 [==============================] - 1s 3ms/step - loss: 0.1608 - accuracy: 0.9456 - val_loss: 0.1570 - val_accuracy: 0.9482\n",
      "\n",
      "Epoch 00114: saving model to ckpt\\keyboard_114.ckpt\n",
      "Epoch 115/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1626 - accuracy: 0.9430 - val_loss: 0.1660 - val_accuracy: 0.9431\n",
      "\n",
      "Epoch 00115: saving model to ckpt\\keyboard_115.ckpt\n",
      "Epoch 116/500\n",
      "183/183 [==============================] - 1s 3ms/step - loss: 0.1632 - accuracy: 0.9439 - val_loss: 0.1558 - val_accuracy: 0.9486\n",
      "\n",
      "Epoch 00116: saving model to ckpt\\keyboard_116.ckpt\n",
      "Epoch 117/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1596 - accuracy: 0.9454 - val_loss: 0.1717 - val_accuracy: 0.9421\n",
      "\n",
      "Epoch 00117: saving model to ckpt\\keyboard_117.ckpt\n",
      "Epoch 118/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1622 - accuracy: 0.9442 - val_loss: 0.1628 - val_accuracy: 0.9446\n",
      "\n",
      "Epoch 00118: saving model to ckpt\\keyboard_118.ckpt\n",
      "Epoch 119/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1617 - accuracy: 0.9445 - val_loss: 0.1588 - val_accuracy: 0.9505\n",
      "\n",
      "Epoch 00119: saving model to ckpt\\keyboard_119.ckpt\n",
      "Epoch 120/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1613 - accuracy: 0.9443 - val_loss: 0.1692 - val_accuracy: 0.9403\n",
      "\n",
      "Epoch 00120: saving model to ckpt\\keyboard_120.ckpt\n",
      "Epoch 121/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1614 - accuracy: 0.9439 - val_loss: 0.1709 - val_accuracy: 0.9400\n",
      "\n",
      "Epoch 00121: saving model to ckpt\\keyboard_121.ckpt\n",
      "Epoch 122/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1621 - accuracy: 0.9438 - val_loss: 0.1655 - val_accuracy: 0.9441\n",
      "\n",
      "Epoch 00122: saving model to ckpt\\keyboard_122.ckpt\n",
      "Epoch 123/500\n",
      "183/183 [==============================] - 1s 3ms/step - loss: 0.1616 - accuracy: 0.9438 - val_loss: 0.1621 - val_accuracy: 0.9465\n",
      "\n",
      "Epoch 00123: saving model to ckpt\\keyboard_123.ckpt\n",
      "Epoch 124/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1602 - accuracy: 0.9441 - val_loss: 0.1612 - val_accuracy: 0.9489\n",
      "\n",
      "Epoch 00124: saving model to ckpt\\keyboard_124.ckpt\n",
      "Epoch 125/500\n",
      "183/183 [==============================] - 1s 3ms/step - loss: 0.1623 - accuracy: 0.9437 - val_loss: 0.1640 - val_accuracy: 0.9436\n",
      "\n",
      "Epoch 00125: saving model to ckpt\\keyboard_125.ckpt\n",
      "Epoch 126/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1605 - accuracy: 0.9448 - val_loss: 0.1590 - val_accuracy: 0.9458\n",
      "\n",
      "Epoch 00126: saving model to ckpt\\keyboard_126.ckpt\n",
      "Epoch 127/500\n",
      "183/183 [==============================] - 1s 3ms/step - loss: 0.1617 - accuracy: 0.9446 - val_loss: 0.1635 - val_accuracy: 0.9441\n",
      "\n",
      "Epoch 00127: saving model to ckpt\\keyboard_127.ckpt\n",
      "Epoch 128/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1628 - accuracy: 0.9438 - val_loss: 0.1643 - val_accuracy: 0.9450\n",
      "\n",
      "Epoch 00128: saving model to ckpt\\keyboard_128.ckpt\n",
      "Epoch 129/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1611 - accuracy: 0.9441 - val_loss: 0.1588 - val_accuracy: 0.9501\n",
      "\n",
      "Epoch 00129: saving model to ckpt\\keyboard_129.ckpt\n",
      "Epoch 130/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1621 - accuracy: 0.9433 - val_loss: 0.1642 - val_accuracy: 0.9433\n",
      "\n",
      "Epoch 00130: saving model to ckpt\\keyboard_130.ckpt\n",
      "Epoch 131/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1605 - accuracy: 0.9449 - val_loss: 0.1545 - val_accuracy: 0.9487\n",
      "\n",
      "Epoch 00131: saving model to ckpt\\keyboard_131.ckpt\n",
      "Epoch 132/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1598 - accuracy: 0.9447 - val_loss: 0.1537 - val_accuracy: 0.9508\n",
      "\n",
      "Epoch 00132: saving model to ckpt\\keyboard_132.ckpt\n",
      "Epoch 133/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1631 - accuracy: 0.9433 - val_loss: 0.1549 - val_accuracy: 0.9474\n",
      "\n",
      "Epoch 00133: saving model to ckpt\\keyboard_133.ckpt\n",
      "Epoch 134/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1619 - accuracy: 0.9445 - val_loss: 0.1664 - val_accuracy: 0.9438\n",
      "\n",
      "Epoch 00134: saving model to ckpt\\keyboard_134.ckpt\n",
      "Epoch 135/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1610 - accuracy: 0.9445 - val_loss: 0.1573 - val_accuracy: 0.9470\n",
      "\n",
      "Epoch 00135: saving model to ckpt\\keyboard_135.ckpt\n",
      "Epoch 136/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1607 - accuracy: 0.9448 - val_loss: 0.1557 - val_accuracy: 0.9481\n",
      "\n",
      "Epoch 00136: saving model to ckpt\\keyboard_136.ckpt\n",
      "Epoch 137/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1622 - accuracy: 0.9439 - val_loss: 0.1551 - val_accuracy: 0.9475\n",
      "\n",
      "Epoch 00137: saving model to ckpt\\keyboard_137.ckpt\n",
      "Epoch 138/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1610 - accuracy: 0.9429 - val_loss: 0.1554 - val_accuracy: 0.9518\n",
      "\n",
      "Epoch 00138: saving model to ckpt\\keyboard_138.ckpt\n",
      "Epoch 139/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1624 - accuracy: 0.9434 - val_loss: 0.1587 - val_accuracy: 0.9484\n",
      "\n",
      "Epoch 00139: saving model to ckpt\\keyboard_139.ckpt\n",
      "Epoch 140/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1612 - accuracy: 0.9447 - val_loss: 0.1572 - val_accuracy: 0.9482\n",
      "\n",
      "Epoch 00140: saving model to ckpt\\keyboard_140.ckpt\n",
      "Epoch 141/500\n",
      "183/183 [==============================] - 1s 3ms/step - loss: 0.1597 - accuracy: 0.9447 - val_loss: 0.1568 - val_accuracy: 0.9479\n",
      "\n",
      "Epoch 00141: saving model to ckpt\\keyboard_141.ckpt\n",
      "Epoch 142/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1609 - accuracy: 0.9439 - val_loss: 0.1568 - val_accuracy: 0.9474\n",
      "\n",
      "Epoch 00142: saving model to ckpt\\keyboard_142.ckpt\n",
      "Epoch 143/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1611 - accuracy: 0.9448 - val_loss: 0.1533 - val_accuracy: 0.9487\n",
      "\n",
      "Epoch 00143: saving model to ckpt\\keyboard_143.ckpt\n",
      "Epoch 144/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1599 - accuracy: 0.9448 - val_loss: 0.1595 - val_accuracy: 0.9474\n",
      "\n",
      "Epoch 00144: saving model to ckpt\\keyboard_144.ckpt\n",
      "Epoch 145/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1596 - accuracy: 0.9450 - val_loss: 0.1623 - val_accuracy: 0.9457\n",
      "\n",
      "Epoch 00145: saving model to ckpt\\keyboard_145.ckpt\n",
      "Epoch 146/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1637 - accuracy: 0.9426 - val_loss: 0.1576 - val_accuracy: 0.9469\n",
      "\n",
      "Epoch 00146: saving model to ckpt\\keyboard_146.ckpt\n",
      "Epoch 147/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1596 - accuracy: 0.9439 - val_loss: 0.1533 - val_accuracy: 0.9506\n",
      "\n",
      "Epoch 00147: saving model to ckpt\\keyboard_147.ckpt\n",
      "Epoch 148/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1615 - accuracy: 0.9449 - val_loss: 0.1694 - val_accuracy: 0.9379\n",
      "\n",
      "Epoch 00148: saving model to ckpt\\keyboard_148.ckpt\n",
      "Epoch 149/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1594 - accuracy: 0.9448 - val_loss: 0.1524 - val_accuracy: 0.9508\n",
      "\n",
      "Epoch 00149: saving model to ckpt\\keyboard_149.ckpt\n",
      "Epoch 150/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1602 - accuracy: 0.9441 - val_loss: 0.1640 - val_accuracy: 0.9475\n",
      "\n",
      "Epoch 00150: saving model to ckpt\\keyboard_150.ckpt\n",
      "Epoch 151/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1604 - accuracy: 0.9446 - val_loss: 0.1649 - val_accuracy: 0.9415\n",
      "\n",
      "Epoch 00151: saving model to ckpt\\keyboard_151.ckpt\n",
      "Epoch 152/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1607 - accuracy: 0.9439 - val_loss: 0.1614 - val_accuracy: 0.9457\n",
      "\n",
      "Epoch 00152: saving model to ckpt\\keyboard_152.ckpt\n",
      "Epoch 153/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1608 - accuracy: 0.9447 - val_loss: 0.1564 - val_accuracy: 0.9491\n",
      "\n",
      "Epoch 00153: saving model to ckpt\\keyboard_153.ckpt\n",
      "Epoch 154/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1622 - accuracy: 0.9432 - val_loss: 0.1531 - val_accuracy: 0.9518\n",
      "\n",
      "Epoch 00154: saving model to ckpt\\keyboard_154.ckpt\n",
      "Epoch 155/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1623 - accuracy: 0.9435 - val_loss: 0.1566 - val_accuracy: 0.9481\n",
      "\n",
      "Epoch 00155: saving model to ckpt\\keyboard_155.ckpt\n",
      "Epoch 156/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1580 - accuracy: 0.9464 - val_loss: 0.1704 - val_accuracy: 0.9405\n",
      "\n",
      "Epoch 00156: saving model to ckpt\\keyboard_156.ckpt\n",
      "Epoch 157/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1597 - accuracy: 0.9443 - val_loss: 0.1645 - val_accuracy: 0.9458\n",
      "\n",
      "Epoch 00157: saving model to ckpt\\keyboard_157.ckpt\n",
      "Epoch 158/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1590 - accuracy: 0.9461 - val_loss: 0.1776 - val_accuracy: 0.9367\n",
      "\n",
      "Epoch 00158: saving model to ckpt\\keyboard_158.ckpt\n",
      "Epoch 159/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1606 - accuracy: 0.9444 - val_loss: 0.1545 - val_accuracy: 0.9482\n",
      "\n",
      "Epoch 00159: saving model to ckpt\\keyboard_159.ckpt\n",
      "Epoch 160/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1615 - accuracy: 0.9435 - val_loss: 0.1616 - val_accuracy: 0.9424\n",
      "\n",
      "Epoch 00160: saving model to ckpt\\keyboard_160.ckpt\n",
      "Epoch 161/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1594 - accuracy: 0.9449 - val_loss: 0.1539 - val_accuracy: 0.9511\n",
      "\n",
      "Epoch 00161: saving model to ckpt\\keyboard_161.ckpt\n",
      "Epoch 162/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1581 - accuracy: 0.9445 - val_loss: 0.1573 - val_accuracy: 0.9481\n",
      "\n",
      "Epoch 00162: saving model to ckpt\\keyboard_162.ckpt\n",
      "Epoch 163/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1586 - accuracy: 0.9447 - val_loss: 0.1525 - val_accuracy: 0.9518\n",
      "\n",
      "Epoch 00163: saving model to ckpt\\keyboard_163.ckpt\n",
      "Epoch 164/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1599 - accuracy: 0.9454 - val_loss: 0.1524 - val_accuracy: 0.9518\n",
      "\n",
      "Epoch 00164: saving model to ckpt\\keyboard_164.ckpt\n",
      "Epoch 165/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1588 - accuracy: 0.9454 - val_loss: 0.1530 - val_accuracy: 0.9511\n",
      "\n",
      "Epoch 00165: saving model to ckpt\\keyboard_165.ckpt\n",
      "Epoch 166/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1592 - accuracy: 0.9463 - val_loss: 0.1522 - val_accuracy: 0.9501\n",
      "\n",
      "Epoch 00166: saving model to ckpt\\keyboard_166.ckpt\n",
      "Epoch 167/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1577 - accuracy: 0.9444 - val_loss: 0.1584 - val_accuracy: 0.9460\n",
      "\n",
      "Epoch 00167: saving model to ckpt\\keyboard_167.ckpt\n",
      "Epoch 168/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1611 - accuracy: 0.9440 - val_loss: 0.1647 - val_accuracy: 0.9434\n",
      "\n",
      "Epoch 00168: saving model to ckpt\\keyboard_168.ckpt\n",
      "Epoch 169/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1620 - accuracy: 0.9439 - val_loss: 0.1587 - val_accuracy: 0.9472\n",
      "\n",
      "Epoch 00169: saving model to ckpt\\keyboard_169.ckpt\n",
      "Epoch 170/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1583 - accuracy: 0.9457 - val_loss: 0.1578 - val_accuracy: 0.9474\n",
      "\n",
      "Epoch 00170: saving model to ckpt\\keyboard_170.ckpt\n",
      "Epoch 171/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1607 - accuracy: 0.9451 - val_loss: 0.1577 - val_accuracy: 0.9494\n",
      "\n",
      "Epoch 00171: saving model to ckpt\\keyboard_171.ckpt\n",
      "Epoch 172/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1587 - accuracy: 0.9450 - val_loss: 0.1537 - val_accuracy: 0.9501\n",
      "\n",
      "Epoch 00172: saving model to ckpt\\keyboard_172.ckpt\n",
      "Epoch 173/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1599 - accuracy: 0.9442 - val_loss: 0.1591 - val_accuracy: 0.9496\n",
      "\n",
      "Epoch 00173: saving model to ckpt\\keyboard_173.ckpt\n",
      "Epoch 174/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1586 - accuracy: 0.9452 - val_loss: 0.1551 - val_accuracy: 0.9493\n",
      "\n",
      "Epoch 00174: saving model to ckpt\\keyboard_174.ckpt\n",
      "Epoch 175/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1578 - accuracy: 0.9452 - val_loss: 0.1533 - val_accuracy: 0.9518\n",
      "\n",
      "Epoch 00175: saving model to ckpt\\keyboard_175.ckpt\n",
      "Epoch 176/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1592 - accuracy: 0.9451 - val_loss: 0.1569 - val_accuracy: 0.9499\n",
      "\n",
      "Epoch 00176: saving model to ckpt\\keyboard_176.ckpt\n",
      "Epoch 177/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1581 - accuracy: 0.9451 - val_loss: 0.1631 - val_accuracy: 0.9446\n",
      "\n",
      "Epoch 00177: saving model to ckpt\\keyboard_177.ckpt\n",
      "Epoch 178/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1606 - accuracy: 0.9449 - val_loss: 0.1568 - val_accuracy: 0.9489\n",
      "\n",
      "Epoch 00178: saving model to ckpt\\keyboard_178.ckpt\n",
      "Epoch 179/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1595 - accuracy: 0.9452 - val_loss: 0.1584 - val_accuracy: 0.9491\n",
      "\n",
      "Epoch 00179: saving model to ckpt\\keyboard_179.ckpt\n",
      "Epoch 180/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1584 - accuracy: 0.9457 - val_loss: 0.1583 - val_accuracy: 0.9472\n",
      "\n",
      "Epoch 00180: saving model to ckpt\\keyboard_180.ckpt\n",
      "Epoch 181/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1585 - accuracy: 0.9449 - val_loss: 0.1533 - val_accuracy: 0.9494\n",
      "\n",
      "Epoch 00181: saving model to ckpt\\keyboard_181.ckpt\n",
      "Epoch 182/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1591 - accuracy: 0.9443 - val_loss: 0.1589 - val_accuracy: 0.9479\n",
      "\n",
      "Epoch 00182: saving model to ckpt\\keyboard_182.ckpt\n",
      "Epoch 183/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1597 - accuracy: 0.9451 - val_loss: 0.1685 - val_accuracy: 0.9426\n",
      "\n",
      "Epoch 00183: saving model to ckpt\\keyboard_183.ckpt\n",
      "Epoch 184/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1597 - accuracy: 0.9451 - val_loss: 0.1579 - val_accuracy: 0.9463\n",
      "\n",
      "Epoch 00184: saving model to ckpt\\keyboard_184.ckpt\n",
      "Epoch 185/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1614 - accuracy: 0.9438 - val_loss: 0.1578 - val_accuracy: 0.9470\n",
      "\n",
      "Epoch 00185: saving model to ckpt\\keyboard_185.ckpt\n",
      "Epoch 186/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1600 - accuracy: 0.9442 - val_loss: 0.1600 - val_accuracy: 0.9486\n",
      "\n",
      "Epoch 00186: saving model to ckpt\\keyboard_186.ckpt\n",
      "Epoch 187/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1583 - accuracy: 0.9458 - val_loss: 0.1563 - val_accuracy: 0.9501\n",
      "\n",
      "Epoch 00187: saving model to ckpt\\keyboard_187.ckpt\n",
      "Epoch 188/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1587 - accuracy: 0.9450 - val_loss: 0.1512 - val_accuracy: 0.9510\n",
      "\n",
      "Epoch 00188: saving model to ckpt\\keyboard_188.ckpt\n",
      "Epoch 189/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1580 - accuracy: 0.9460 - val_loss: 0.1541 - val_accuracy: 0.9491\n",
      "\n",
      "Epoch 00189: saving model to ckpt\\keyboard_189.ckpt\n",
      "Epoch 190/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1594 - accuracy: 0.9451 - val_loss: 0.1620 - val_accuracy: 0.9453\n",
      "\n",
      "Epoch 00190: saving model to ckpt\\keyboard_190.ckpt\n",
      "Epoch 191/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1590 - accuracy: 0.9448 - val_loss: 0.1554 - val_accuracy: 0.9482\n",
      "\n",
      "Epoch 00191: saving model to ckpt\\keyboard_191.ckpt\n",
      "Epoch 192/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1573 - accuracy: 0.9460 - val_loss: 0.1540 - val_accuracy: 0.9501\n",
      "\n",
      "Epoch 00192: saving model to ckpt\\keyboard_192.ckpt\n",
      "Epoch 193/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1593 - accuracy: 0.9449 - val_loss: 0.1603 - val_accuracy: 0.9481\n",
      "\n",
      "Epoch 00193: saving model to ckpt\\keyboard_193.ckpt\n",
      "Epoch 194/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1608 - accuracy: 0.9435 - val_loss: 0.1680 - val_accuracy: 0.9410\n",
      "\n",
      "Epoch 00194: saving model to ckpt\\keyboard_194.ckpt\n",
      "Epoch 195/500\n",
      "183/183 [==============================] - 1s 6ms/step - loss: 0.1585 - accuracy: 0.9448 - val_loss: 0.1609 - val_accuracy: 0.9481\n",
      "\n",
      "Epoch 00195: saving model to ckpt\\keyboard_195.ckpt\n",
      "Epoch 196/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1587 - accuracy: 0.9451 - val_loss: 0.1607 - val_accuracy: 0.9458\n",
      "\n",
      "Epoch 00196: saving model to ckpt\\keyboard_196.ckpt\n",
      "Epoch 197/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1576 - accuracy: 0.9454 - val_loss: 0.1551 - val_accuracy: 0.9501\n",
      "\n",
      "Epoch 00197: saving model to ckpt\\keyboard_197.ckpt\n",
      "Epoch 198/500\n",
      "183/183 [==============================] - 1s 3ms/step - loss: 0.1592 - accuracy: 0.9448 - val_loss: 0.1567 - val_accuracy: 0.9489\n",
      "\n",
      "Epoch 00198: saving model to ckpt\\keyboard_198.ckpt\n",
      "Epoch 199/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1570 - accuracy: 0.9459 - val_loss: 0.1514 - val_accuracy: 0.9505\n",
      "\n",
      "Epoch 00199: saving model to ckpt\\keyboard_199.ckpt\n",
      "Epoch 200/500\n",
      "183/183 [==============================] - 1s 3ms/step - loss: 0.1570 - accuracy: 0.9456 - val_loss: 0.1671 - val_accuracy: 0.9417\n",
      "\n",
      "Epoch 00200: saving model to ckpt\\keyboard_200.ckpt\n",
      "Epoch 201/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1578 - accuracy: 0.9456 - val_loss: 0.1557 - val_accuracy: 0.9470\n",
      "\n",
      "Epoch 00201: saving model to ckpt\\keyboard_201.ckpt\n",
      "Epoch 202/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1585 - accuracy: 0.9443 - val_loss: 0.1513 - val_accuracy: 0.9508\n",
      "\n",
      "Epoch 00202: saving model to ckpt\\keyboard_202.ckpt\n",
      "Epoch 203/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1588 - accuracy: 0.9443 - val_loss: 0.1555 - val_accuracy: 0.9484\n",
      "\n",
      "Epoch 00203: saving model to ckpt\\keyboard_203.ckpt\n",
      "Epoch 204/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1561 - accuracy: 0.9463 - val_loss: 0.1549 - val_accuracy: 0.9501\n",
      "\n",
      "Epoch 00204: saving model to ckpt\\keyboard_204.ckpt\n",
      "Epoch 205/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1591 - accuracy: 0.9455 - val_loss: 0.1508 - val_accuracy: 0.9505\n",
      "\n",
      "Epoch 00205: saving model to ckpt\\keyboard_205.ckpt\n",
      "Epoch 206/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1600 - accuracy: 0.9451 - val_loss: 0.1537 - val_accuracy: 0.9486\n",
      "\n",
      "Epoch 00206: saving model to ckpt\\keyboard_206.ckpt\n",
      "Epoch 207/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1592 - accuracy: 0.9447 - val_loss: 0.1625 - val_accuracy: 0.9458\n",
      "\n",
      "Epoch 00207: saving model to ckpt\\keyboard_207.ckpt\n",
      "Epoch 208/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1566 - accuracy: 0.9467 - val_loss: 0.1577 - val_accuracy: 0.9474\n",
      "\n",
      "Epoch 00208: saving model to ckpt\\keyboard_208.ckpt\n",
      "Epoch 209/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1581 - accuracy: 0.9452 - val_loss: 0.1550 - val_accuracy: 0.9487\n",
      "\n",
      "Epoch 00209: saving model to ckpt\\keyboard_209.ckpt\n",
      "Epoch 210/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1582 - accuracy: 0.9454 - val_loss: 0.1521 - val_accuracy: 0.9513\n",
      "\n",
      "Epoch 00210: saving model to ckpt\\keyboard_210.ckpt\n",
      "Epoch 211/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1584 - accuracy: 0.9445 - val_loss: 0.1668 - val_accuracy: 0.9415\n",
      "\n",
      "Epoch 00211: saving model to ckpt\\keyboard_211.ckpt\n",
      "Epoch 212/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1598 - accuracy: 0.9444 - val_loss: 0.1525 - val_accuracy: 0.9493\n",
      "\n",
      "Epoch 00212: saving model to ckpt\\keyboard_212.ckpt\n",
      "Epoch 213/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1565 - accuracy: 0.9461 - val_loss: 0.1647 - val_accuracy: 0.9412\n",
      "\n",
      "Epoch 00213: saving model to ckpt\\keyboard_213.ckpt\n",
      "Epoch 214/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1573 - accuracy: 0.9465 - val_loss: 0.1534 - val_accuracy: 0.9494\n",
      "\n",
      "Epoch 00214: saving model to ckpt\\keyboard_214.ckpt\n",
      "Epoch 215/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1575 - accuracy: 0.9452 - val_loss: 0.1549 - val_accuracy: 0.9493\n",
      "\n",
      "Epoch 00215: saving model to ckpt\\keyboard_215.ckpt\n",
      "Epoch 216/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1585 - accuracy: 0.9455 - val_loss: 0.1536 - val_accuracy: 0.9487\n",
      "\n",
      "Epoch 00216: saving model to ckpt\\keyboard_216.ckpt\n",
      "Epoch 217/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1572 - accuracy: 0.9457 - val_loss: 0.1644 - val_accuracy: 0.9427\n",
      "\n",
      "Epoch 00217: saving model to ckpt\\keyboard_217.ckpt\n",
      "Epoch 218/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1602 - accuracy: 0.9430 - val_loss: 0.1566 - val_accuracy: 0.9491\n",
      "\n",
      "Epoch 00218: saving model to ckpt\\keyboard_218.ckpt\n",
      "Epoch 219/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1582 - accuracy: 0.9454 - val_loss: 0.1698 - val_accuracy: 0.9397\n",
      "\n",
      "Epoch 00219: saving model to ckpt\\keyboard_219.ckpt\n",
      "Epoch 220/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1573 - accuracy: 0.9454 - val_loss: 0.1571 - val_accuracy: 0.9462\n",
      "\n",
      "Epoch 00220: saving model to ckpt\\keyboard_220.ckpt\n",
      "Epoch 221/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1583 - accuracy: 0.9451 - val_loss: 0.1526 - val_accuracy: 0.9501\n",
      "\n",
      "Epoch 00221: saving model to ckpt\\keyboard_221.ckpt\n",
      "Epoch 222/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1579 - accuracy: 0.9445 - val_loss: 0.1532 - val_accuracy: 0.9494\n",
      "\n",
      "Epoch 00222: saving model to ckpt\\keyboard_222.ckpt\n",
      "Epoch 223/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1580 - accuracy: 0.9457 - val_loss: 0.1502 - val_accuracy: 0.9525\n",
      "\n",
      "Epoch 00223: saving model to ckpt\\keyboard_223.ckpt\n",
      "Epoch 224/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1592 - accuracy: 0.9442 - val_loss: 0.1570 - val_accuracy: 0.9482\n",
      "\n",
      "Epoch 00224: saving model to ckpt\\keyboard_224.ckpt\n",
      "Epoch 225/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1564 - accuracy: 0.9467 - val_loss: 0.1540 - val_accuracy: 0.9475\n",
      "\n",
      "Epoch 00225: saving model to ckpt\\keyboard_225.ckpt\n",
      "Epoch 226/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1549 - accuracy: 0.9465 - val_loss: 0.1539 - val_accuracy: 0.9482\n",
      "\n",
      "Epoch 00226: saving model to ckpt\\keyboard_226.ckpt\n",
      "Epoch 227/500\n",
      "183/183 [==============================] - 1s 3ms/step - loss: 0.1575 - accuracy: 0.9465 - val_loss: 0.1513 - val_accuracy: 0.9506\n",
      "\n",
      "Epoch 00227: saving model to ckpt\\keyboard_227.ckpt\n",
      "Epoch 228/500\n",
      "183/183 [==============================] - 1s 6ms/step - loss: 0.1570 - accuracy: 0.9471 - val_loss: 0.1602 - val_accuracy: 0.9463\n",
      "\n",
      "Epoch 00228: saving model to ckpt\\keyboard_228.ckpt\n",
      "Epoch 229/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1586 - accuracy: 0.9450 - val_loss: 0.1592 - val_accuracy: 0.9474\n",
      "\n",
      "Epoch 00229: saving model to ckpt\\keyboard_229.ckpt\n",
      "Epoch 230/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1575 - accuracy: 0.9459 - val_loss: 0.1572 - val_accuracy: 0.9477\n",
      "\n",
      "Epoch 00230: saving model to ckpt\\keyboard_230.ckpt\n",
      "Epoch 231/500\n",
      "183/183 [==============================] - 1s 6ms/step - loss: 0.1572 - accuracy: 0.9451 - val_loss: 0.1595 - val_accuracy: 0.9450\n",
      "\n",
      "Epoch 00231: saving model to ckpt\\keyboard_231.ckpt\n",
      "Epoch 232/500\n",
      "183/183 [==============================] - 1s 6ms/step - loss: 0.1577 - accuracy: 0.9454 - val_loss: 0.1614 - val_accuracy: 0.9443\n",
      "\n",
      "Epoch 00232: saving model to ckpt\\keyboard_232.ckpt\n",
      "Epoch 233/500\n",
      "183/183 [==============================] - 1s 6ms/step - loss: 0.1574 - accuracy: 0.9452 - val_loss: 0.1525 - val_accuracy: 0.9501\n",
      "\n",
      "Epoch 00233: saving model to ckpt\\keyboard_233.ckpt\n",
      "Epoch 234/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1572 - accuracy: 0.9448 - val_loss: 0.1543 - val_accuracy: 0.9486\n",
      "\n",
      "Epoch 00234: saving model to ckpt\\keyboard_234.ckpt\n",
      "Epoch 235/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1588 - accuracy: 0.9456 - val_loss: 0.1555 - val_accuracy: 0.9508\n",
      "\n",
      "Epoch 00235: saving model to ckpt\\keyboard_235.ckpt\n",
      "Epoch 236/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1562 - accuracy: 0.9468 - val_loss: 0.1534 - val_accuracy: 0.9496\n",
      "\n",
      "Epoch 00236: saving model to ckpt\\keyboard_236.ckpt\n",
      "Epoch 237/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1582 - accuracy: 0.9453 - val_loss: 0.1596 - val_accuracy: 0.9458\n",
      "\n",
      "Epoch 00237: saving model to ckpt\\keyboard_237.ckpt\n",
      "Epoch 238/500\n",
      "183/183 [==============================] - 1s 6ms/step - loss: 0.1593 - accuracy: 0.9443 - val_loss: 0.1526 - val_accuracy: 0.9477\n",
      "\n",
      "Epoch 00238: saving model to ckpt\\keyboard_238.ckpt\n",
      "Epoch 239/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1575 - accuracy: 0.9462 - val_loss: 0.1744 - val_accuracy: 0.9373\n",
      "\n",
      "Epoch 00239: saving model to ckpt\\keyboard_239.ckpt\n",
      "Epoch 240/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1601 - accuracy: 0.9439 - val_loss: 0.1713 - val_accuracy: 0.9431\n",
      "\n",
      "Epoch 00240: saving model to ckpt\\keyboard_240.ckpt\n",
      "Epoch 241/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1596 - accuracy: 0.9428 - val_loss: 0.1530 - val_accuracy: 0.9499\n",
      "\n",
      "Epoch 00241: saving model to ckpt\\keyboard_241.ckpt\n",
      "Epoch 242/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1546 - accuracy: 0.9472 - val_loss: 0.1544 - val_accuracy: 0.9491\n",
      "\n",
      "Epoch 00242: saving model to ckpt\\keyboard_242.ckpt\n",
      "Epoch 243/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1564 - accuracy: 0.9454 - val_loss: 0.1511 - val_accuracy: 0.9517\n",
      "\n",
      "Epoch 00243: saving model to ckpt\\keyboard_243.ckpt\n",
      "Epoch 244/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1576 - accuracy: 0.9448 - val_loss: 0.1518 - val_accuracy: 0.9506\n",
      "\n",
      "Epoch 00244: saving model to ckpt\\keyboard_244.ckpt\n",
      "Epoch 245/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1562 - accuracy: 0.9460 - val_loss: 0.1531 - val_accuracy: 0.9503\n",
      "\n",
      "Epoch 00245: saving model to ckpt\\keyboard_245.ckpt\n",
      "Epoch 246/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1560 - accuracy: 0.9457 - val_loss: 0.1609 - val_accuracy: 0.9453\n",
      "\n",
      "Epoch 00246: saving model to ckpt\\keyboard_246.ckpt\n",
      "Epoch 247/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1561 - accuracy: 0.9457 - val_loss: 0.1498 - val_accuracy: 0.9515\n",
      "\n",
      "Epoch 00247: saving model to ckpt\\keyboard_247.ckpt\n",
      "Epoch 248/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1561 - accuracy: 0.9472 - val_loss: 0.1505 - val_accuracy: 0.9505\n",
      "\n",
      "Epoch 00248: saving model to ckpt\\keyboard_248.ckpt\n",
      "Epoch 249/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1588 - accuracy: 0.9449 - val_loss: 0.1523 - val_accuracy: 0.9494\n",
      "\n",
      "Epoch 00249: saving model to ckpt\\keyboard_249.ckpt\n",
      "Epoch 250/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1590 - accuracy: 0.9446 - val_loss: 0.1522 - val_accuracy: 0.9503\n",
      "\n",
      "Epoch 00250: saving model to ckpt\\keyboard_250.ckpt\n",
      "Epoch 251/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1578 - accuracy: 0.9444 - val_loss: 0.1567 - val_accuracy: 0.9458\n",
      "\n",
      "Epoch 00251: saving model to ckpt\\keyboard_251.ckpt\n",
      "Epoch 252/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1582 - accuracy: 0.9451 - val_loss: 0.1575 - val_accuracy: 0.9482\n",
      "\n",
      "Epoch 00252: saving model to ckpt\\keyboard_252.ckpt\n",
      "Epoch 253/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1561 - accuracy: 0.9457 - val_loss: 0.1528 - val_accuracy: 0.9510\n",
      "\n",
      "Epoch 00253: saving model to ckpt\\keyboard_253.ckpt\n",
      "Epoch 254/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1565 - accuracy: 0.9456 - val_loss: 0.1548 - val_accuracy: 0.9486\n",
      "\n",
      "Epoch 00254: saving model to ckpt\\keyboard_254.ckpt\n",
      "Epoch 255/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1577 - accuracy: 0.9451 - val_loss: 0.1561 - val_accuracy: 0.9472\n",
      "\n",
      "Epoch 00255: saving model to ckpt\\keyboard_255.ckpt\n",
      "Epoch 256/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1552 - accuracy: 0.9454 - val_loss: 0.1524 - val_accuracy: 0.9487\n",
      "\n",
      "Epoch 00256: saving model to ckpt\\keyboard_256.ckpt\n",
      "Epoch 257/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1577 - accuracy: 0.9451 - val_loss: 0.1621 - val_accuracy: 0.9409\n",
      "\n",
      "Epoch 00257: saving model to ckpt\\keyboard_257.ckpt\n",
      "Epoch 258/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1580 - accuracy: 0.9446 - val_loss: 0.1542 - val_accuracy: 0.9503\n",
      "\n",
      "Epoch 00258: saving model to ckpt\\keyboard_258.ckpt\n",
      "Epoch 259/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1552 - accuracy: 0.9464 - val_loss: 0.1534 - val_accuracy: 0.9486\n",
      "\n",
      "Epoch 00259: saving model to ckpt\\keyboard_259.ckpt\n",
      "Epoch 260/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1555 - accuracy: 0.9466 - val_loss: 0.1668 - val_accuracy: 0.9451\n",
      "\n",
      "Epoch 00260: saving model to ckpt\\keyboard_260.ckpt\n",
      "Epoch 261/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1565 - accuracy: 0.9449 - val_loss: 0.1560 - val_accuracy: 0.9469\n",
      "\n",
      "Epoch 00261: saving model to ckpt\\keyboard_261.ckpt\n",
      "Epoch 262/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1576 - accuracy: 0.9445 - val_loss: 0.1574 - val_accuracy: 0.9448\n",
      "\n",
      "Epoch 00262: saving model to ckpt\\keyboard_262.ckpt\n",
      "Epoch 263/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1571 - accuracy: 0.9454 - val_loss: 0.1518 - val_accuracy: 0.9494\n",
      "\n",
      "Epoch 00263: saving model to ckpt\\keyboard_263.ckpt\n",
      "Epoch 264/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1566 - accuracy: 0.9459 - val_loss: 0.1609 - val_accuracy: 0.9460\n",
      "\n",
      "Epoch 00264: saving model to ckpt\\keyboard_264.ckpt\n",
      "Epoch 265/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1591 - accuracy: 0.9450 - val_loss: 0.1501 - val_accuracy: 0.9518\n",
      "\n",
      "Epoch 00265: saving model to ckpt\\keyboard_265.ckpt\n",
      "Epoch 266/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1546 - accuracy: 0.9464 - val_loss: 0.1603 - val_accuracy: 0.9448\n",
      "\n",
      "Epoch 00266: saving model to ckpt\\keyboard_266.ckpt\n",
      "Epoch 267/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1571 - accuracy: 0.9456 - val_loss: 0.1514 - val_accuracy: 0.9489\n",
      "\n",
      "Epoch 00267: saving model to ckpt\\keyboard_267.ckpt\n",
      "Epoch 268/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1562 - accuracy: 0.9455 - val_loss: 0.1623 - val_accuracy: 0.9458\n",
      "\n",
      "Epoch 00268: saving model to ckpt\\keyboard_268.ckpt\n",
      "Epoch 269/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1571 - accuracy: 0.9462 - val_loss: 0.1623 - val_accuracy: 0.9433\n",
      "\n",
      "Epoch 00269: saving model to ckpt\\keyboard_269.ckpt\n",
      "Epoch 270/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1561 - accuracy: 0.9464 - val_loss: 0.1504 - val_accuracy: 0.9506\n",
      "\n",
      "Epoch 00270: saving model to ckpt\\keyboard_270.ckpt\n",
      "Epoch 271/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1563 - accuracy: 0.9453 - val_loss: 0.1525 - val_accuracy: 0.9491\n",
      "\n",
      "Epoch 00271: saving model to ckpt\\keyboard_271.ckpt\n",
      "Epoch 272/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1572 - accuracy: 0.9454 - val_loss: 0.1549 - val_accuracy: 0.9501\n",
      "\n",
      "Epoch 00272: saving model to ckpt\\keyboard_272.ckpt\n",
      "Epoch 273/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1558 - accuracy: 0.9462 - val_loss: 0.1640 - val_accuracy: 0.9434\n",
      "\n",
      "Epoch 00273: saving model to ckpt\\keyboard_273.ckpt\n",
      "Epoch 274/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1574 - accuracy: 0.9452 - val_loss: 0.1519 - val_accuracy: 0.9498\n",
      "\n",
      "Epoch 00274: saving model to ckpt\\keyboard_274.ckpt\n",
      "Epoch 275/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1561 - accuracy: 0.9464 - val_loss: 0.1517 - val_accuracy: 0.9491\n",
      "\n",
      "Epoch 00275: saving model to ckpt\\keyboard_275.ckpt\n",
      "Epoch 276/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1555 - accuracy: 0.9463 - val_loss: 0.1591 - val_accuracy: 0.9438\n",
      "\n",
      "Epoch 00276: saving model to ckpt\\keyboard_276.ckpt\n",
      "Epoch 277/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1563 - accuracy: 0.9455 - val_loss: 0.1605 - val_accuracy: 0.9451\n",
      "\n",
      "Epoch 00277: saving model to ckpt\\keyboard_277.ckpt\n",
      "Epoch 278/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1555 - accuracy: 0.9460 - val_loss: 0.1692 - val_accuracy: 0.9397\n",
      "\n",
      "Epoch 00278: saving model to ckpt\\keyboard_278.ckpt\n",
      "Epoch 279/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1568 - accuracy: 0.9451 - val_loss: 0.1581 - val_accuracy: 0.9455\n",
      "\n",
      "Epoch 00279: saving model to ckpt\\keyboard_279.ckpt\n",
      "Epoch 280/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1576 - accuracy: 0.9446 - val_loss: 0.1486 - val_accuracy: 0.9527\n",
      "\n",
      "Epoch 00280: saving model to ckpt\\keyboard_280.ckpt\n",
      "Epoch 281/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1541 - accuracy: 0.9457 - val_loss: 0.1648 - val_accuracy: 0.9417\n",
      "\n",
      "Epoch 00281: saving model to ckpt\\keyboard_281.ckpt\n",
      "Epoch 282/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1581 - accuracy: 0.9458 - val_loss: 0.1519 - val_accuracy: 0.9508\n",
      "\n",
      "Epoch 00282: saving model to ckpt\\keyboard_282.ckpt\n",
      "Epoch 283/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1567 - accuracy: 0.9454 - val_loss: 0.1502 - val_accuracy: 0.9503\n",
      "\n",
      "Epoch 00283: saving model to ckpt\\keyboard_283.ckpt\n",
      "Epoch 284/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1569 - accuracy: 0.9458 - val_loss: 0.1672 - val_accuracy: 0.9402\n",
      "\n",
      "Epoch 00284: saving model to ckpt\\keyboard_284.ckpt\n",
      "Epoch 285/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1548 - accuracy: 0.9472 - val_loss: 0.1594 - val_accuracy: 0.9489\n",
      "\n",
      "Epoch 00285: saving model to ckpt\\keyboard_285.ckpt\n",
      "Epoch 286/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1574 - accuracy: 0.9455 - val_loss: 0.1541 - val_accuracy: 0.9498\n",
      "\n",
      "Epoch 00286: saving model to ckpt\\keyboard_286.ckpt\n",
      "Epoch 287/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1565 - accuracy: 0.9458 - val_loss: 0.1577 - val_accuracy: 0.9474\n",
      "\n",
      "Epoch 00287: saving model to ckpt\\keyboard_287.ckpt\n",
      "Epoch 288/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1554 - accuracy: 0.9455 - val_loss: 0.1478 - val_accuracy: 0.9522\n",
      "\n",
      "Epoch 00288: saving model to ckpt\\keyboard_288.ckpt\n",
      "Epoch 289/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1536 - accuracy: 0.9467 - val_loss: 0.1505 - val_accuracy: 0.9508\n",
      "\n",
      "Epoch 00289: saving model to ckpt\\keyboard_289.ckpt\n",
      "Epoch 290/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1541 - accuracy: 0.9472 - val_loss: 0.1616 - val_accuracy: 0.9439\n",
      "\n",
      "Epoch 00290: saving model to ckpt\\keyboard_290.ckpt\n",
      "Epoch 291/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1560 - accuracy: 0.9464 - val_loss: 0.1517 - val_accuracy: 0.9498\n",
      "\n",
      "Epoch 00291: saving model to ckpt\\keyboard_291.ckpt\n",
      "Epoch 292/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1549 - accuracy: 0.9461 - val_loss: 0.1546 - val_accuracy: 0.9493\n",
      "\n",
      "Epoch 00292: saving model to ckpt\\keyboard_292.ckpt\n",
      "Epoch 293/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1566 - accuracy: 0.9462 - val_loss: 0.1614 - val_accuracy: 0.9455\n",
      "\n",
      "Epoch 00293: saving model to ckpt\\keyboard_293.ckpt\n",
      "Epoch 294/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1587 - accuracy: 0.9444 - val_loss: 0.1480 - val_accuracy: 0.9525\n",
      "\n",
      "Epoch 00294: saving model to ckpt\\keyboard_294.ckpt\n",
      "Epoch 295/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1562 - accuracy: 0.9464 - val_loss: 0.1513 - val_accuracy: 0.9513\n",
      "\n",
      "Epoch 00295: saving model to ckpt\\keyboard_295.ckpt\n",
      "Epoch 296/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1569 - accuracy: 0.9456 - val_loss: 0.1649 - val_accuracy: 0.9431\n",
      "\n",
      "Epoch 00296: saving model to ckpt\\keyboard_296.ckpt\n",
      "Epoch 297/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1562 - accuracy: 0.9464 - val_loss: 0.1542 - val_accuracy: 0.9482\n",
      "\n",
      "Epoch 00297: saving model to ckpt\\keyboard_297.ckpt\n",
      "Epoch 298/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1560 - accuracy: 0.9437 - val_loss: 0.1483 - val_accuracy: 0.9508\n",
      "\n",
      "Epoch 00298: saving model to ckpt\\keyboard_298.ckpt\n",
      "Epoch 299/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1531 - accuracy: 0.9468 - val_loss: 0.1552 - val_accuracy: 0.9474\n",
      "\n",
      "Epoch 00299: saving model to ckpt\\keyboard_299.ckpt\n",
      "Epoch 300/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1557 - accuracy: 0.9460 - val_loss: 0.1513 - val_accuracy: 0.9506\n",
      "\n",
      "Epoch 00300: saving model to ckpt\\keyboard_300.ckpt\n",
      "Epoch 301/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1554 - accuracy: 0.9459 - val_loss: 0.1571 - val_accuracy: 0.9470\n",
      "\n",
      "Epoch 00301: saving model to ckpt\\keyboard_301.ckpt\n",
      "Epoch 302/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1567 - accuracy: 0.9446 - val_loss: 0.1566 - val_accuracy: 0.9455\n",
      "\n",
      "Epoch 00302: saving model to ckpt\\keyboard_302.ckpt\n",
      "Epoch 303/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1553 - accuracy: 0.9461 - val_loss: 0.1549 - val_accuracy: 0.9494\n",
      "\n",
      "Epoch 00303: saving model to ckpt\\keyboard_303.ckpt\n",
      "Epoch 304/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1574 - accuracy: 0.9446 - val_loss: 0.1496 - val_accuracy: 0.9503\n",
      "\n",
      "Epoch 00304: saving model to ckpt\\keyboard_304.ckpt\n",
      "Epoch 305/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1548 - accuracy: 0.9463 - val_loss: 0.1572 - val_accuracy: 0.9450\n",
      "\n",
      "Epoch 00305: saving model to ckpt\\keyboard_305.ckpt\n",
      "Epoch 306/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1574 - accuracy: 0.9454 - val_loss: 0.1528 - val_accuracy: 0.9501\n",
      "\n",
      "Epoch 00306: saving model to ckpt\\keyboard_306.ckpt\n",
      "Epoch 307/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1549 - accuracy: 0.9463 - val_loss: 0.1506 - val_accuracy: 0.9503\n",
      "\n",
      "Epoch 00307: saving model to ckpt\\keyboard_307.ckpt\n",
      "Epoch 308/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1539 - accuracy: 0.9471 - val_loss: 0.1485 - val_accuracy: 0.9527\n",
      "\n",
      "Epoch 00308: saving model to ckpt\\keyboard_308.ckpt\n",
      "Epoch 309/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1549 - accuracy: 0.9463 - val_loss: 0.1512 - val_accuracy: 0.9489\n",
      "\n",
      "Epoch 00309: saving model to ckpt\\keyboard_309.ckpt\n",
      "Epoch 310/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1549 - accuracy: 0.9461 - val_loss: 0.1509 - val_accuracy: 0.9515\n",
      "\n",
      "Epoch 00310: saving model to ckpt\\keyboard_310.ckpt\n",
      "Epoch 311/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1531 - accuracy: 0.9470 - val_loss: 0.1580 - val_accuracy: 0.9481\n",
      "\n",
      "Epoch 00311: saving model to ckpt\\keyboard_311.ckpt\n",
      "Epoch 312/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1571 - accuracy: 0.9455 - val_loss: 0.1504 - val_accuracy: 0.9503\n",
      "\n",
      "Epoch 00312: saving model to ckpt\\keyboard_312.ckpt\n",
      "Epoch 313/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1546 - accuracy: 0.9465 - val_loss: 0.1551 - val_accuracy: 0.9499\n",
      "\n",
      "Epoch 00313: saving model to ckpt\\keyboard_313.ckpt\n",
      "Epoch 314/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1538 - accuracy: 0.9464 - val_loss: 0.1573 - val_accuracy: 0.9474\n",
      "\n",
      "Epoch 00314: saving model to ckpt\\keyboard_314.ckpt\n",
      "Epoch 315/500\n",
      "183/183 [==============================] - 1s 3ms/step - loss: 0.1542 - accuracy: 0.9467 - val_loss: 0.1527 - val_accuracy: 0.9489\n",
      "\n",
      "Epoch 00315: saving model to ckpt\\keyboard_315.ckpt\n",
      "Epoch 316/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1556 - accuracy: 0.9457 - val_loss: 0.1596 - val_accuracy: 0.9451\n",
      "\n",
      "Epoch 00316: saving model to ckpt\\keyboard_316.ckpt\n",
      "Epoch 317/500\n",
      "183/183 [==============================] - 1s 3ms/step - loss: 0.1554 - accuracy: 0.9457 - val_loss: 0.1472 - val_accuracy: 0.9530\n",
      "\n",
      "Epoch 00317: saving model to ckpt\\keyboard_317.ckpt\n",
      "Epoch 318/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1540 - accuracy: 0.9471 - val_loss: 0.1533 - val_accuracy: 0.9487\n",
      "\n",
      "Epoch 00318: saving model to ckpt\\keyboard_318.ckpt\n",
      "Epoch 319/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1564 - accuracy: 0.9457 - val_loss: 0.1533 - val_accuracy: 0.9494\n",
      "\n",
      "Epoch 00319: saving model to ckpt\\keyboard_319.ckpt\n",
      "Epoch 320/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1550 - accuracy: 0.9462 - val_loss: 0.1535 - val_accuracy: 0.9503\n",
      "\n",
      "Epoch 00320: saving model to ckpt\\keyboard_320.ckpt\n",
      "Epoch 321/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1545 - accuracy: 0.9462 - val_loss: 0.1493 - val_accuracy: 0.9522\n",
      "\n",
      "Epoch 00321: saving model to ckpt\\keyboard_321.ckpt\n",
      "Epoch 322/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1553 - accuracy: 0.9467 - val_loss: 0.1522 - val_accuracy: 0.9487\n",
      "\n",
      "Epoch 00322: saving model to ckpt\\keyboard_322.ckpt\n",
      "Epoch 323/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1552 - accuracy: 0.9461 - val_loss: 0.1579 - val_accuracy: 0.9472\n",
      "\n",
      "Epoch 00323: saving model to ckpt\\keyboard_323.ckpt\n",
      "Epoch 324/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1579 - accuracy: 0.9458 - val_loss: 0.1523 - val_accuracy: 0.9505\n",
      "\n",
      "Epoch 00324: saving model to ckpt\\keyboard_324.ckpt\n",
      "Epoch 325/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1556 - accuracy: 0.9451 - val_loss: 0.1543 - val_accuracy: 0.9474\n",
      "\n",
      "Epoch 00325: saving model to ckpt\\keyboard_325.ckpt\n",
      "Epoch 326/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1542 - accuracy: 0.9462 - val_loss: 0.1570 - val_accuracy: 0.9467\n",
      "\n",
      "Epoch 00326: saving model to ckpt\\keyboard_326.ckpt\n",
      "Epoch 327/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1547 - accuracy: 0.9464 - val_loss: 0.1533 - val_accuracy: 0.9489\n",
      "\n",
      "Epoch 00327: saving model to ckpt\\keyboard_327.ckpt\n",
      "Epoch 328/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1537 - accuracy: 0.9467 - val_loss: 0.1557 - val_accuracy: 0.9462\n",
      "\n",
      "Epoch 00328: saving model to ckpt\\keyboard_328.ckpt\n",
      "Epoch 329/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1544 - accuracy: 0.9471 - val_loss: 0.1728 - val_accuracy: 0.9424\n",
      "\n",
      "Epoch 00329: saving model to ckpt\\keyboard_329.ckpt\n",
      "Epoch 330/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1569 - accuracy: 0.9455 - val_loss: 0.1487 - val_accuracy: 0.9493\n",
      "\n",
      "Epoch 00330: saving model to ckpt\\keyboard_330.ckpt\n",
      "Epoch 331/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1562 - accuracy: 0.9449 - val_loss: 0.1487 - val_accuracy: 0.9508\n",
      "\n",
      "Epoch 00331: saving model to ckpt\\keyboard_331.ckpt\n",
      "Epoch 332/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1586 - accuracy: 0.9436 - val_loss: 0.1562 - val_accuracy: 0.9462\n",
      "\n",
      "Epoch 00332: saving model to ckpt\\keyboard_332.ckpt\n",
      "Epoch 333/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1557 - accuracy: 0.9460 - val_loss: 0.1527 - val_accuracy: 0.9479\n",
      "\n",
      "Epoch 00333: saving model to ckpt\\keyboard_333.ckpt\n",
      "Epoch 334/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1549 - accuracy: 0.9463 - val_loss: 0.1484 - val_accuracy: 0.9511\n",
      "\n",
      "Epoch 00334: saving model to ckpt\\keyboard_334.ckpt\n",
      "Epoch 335/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1556 - accuracy: 0.9455 - val_loss: 0.1563 - val_accuracy: 0.9481\n",
      "\n",
      "Epoch 00335: saving model to ckpt\\keyboard_335.ckpt\n",
      "Epoch 336/500\n",
      "183/183 [==============================] - 1s 3ms/step - loss: 0.1554 - accuracy: 0.9466 - val_loss: 0.1528 - val_accuracy: 0.9493\n",
      "\n",
      "Epoch 00336: saving model to ckpt\\keyboard_336.ckpt\n",
      "Epoch 337/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1535 - accuracy: 0.9469 - val_loss: 0.1485 - val_accuracy: 0.9505\n",
      "\n",
      "Epoch 00337: saving model to ckpt\\keyboard_337.ckpt\n",
      "Epoch 338/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1556 - accuracy: 0.9464 - val_loss: 0.1504 - val_accuracy: 0.9517\n",
      "\n",
      "Epoch 00338: saving model to ckpt\\keyboard_338.ckpt\n",
      "Epoch 339/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1560 - accuracy: 0.9459 - val_loss: 0.1577 - val_accuracy: 0.9487\n",
      "\n",
      "Epoch 00339: saving model to ckpt\\keyboard_339.ckpt\n",
      "Epoch 340/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1558 - accuracy: 0.9468 - val_loss: 0.1531 - val_accuracy: 0.9518\n",
      "\n",
      "Epoch 00340: saving model to ckpt\\keyboard_340.ckpt\n",
      "Epoch 341/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1544 - accuracy: 0.9470 - val_loss: 0.1487 - val_accuracy: 0.9496\n",
      "\n",
      "Epoch 00341: saving model to ckpt\\keyboard_341.ckpt\n",
      "Epoch 342/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1553 - accuracy: 0.9451 - val_loss: 0.1496 - val_accuracy: 0.9506\n",
      "\n",
      "Epoch 00342: saving model to ckpt\\keyboard_342.ckpt\n",
      "Epoch 343/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1546 - accuracy: 0.9459 - val_loss: 0.1525 - val_accuracy: 0.9475\n",
      "\n",
      "Epoch 00343: saving model to ckpt\\keyboard_343.ckpt\n",
      "Epoch 344/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1550 - accuracy: 0.9459 - val_loss: 0.1537 - val_accuracy: 0.9489\n",
      "\n",
      "Epoch 00344: saving model to ckpt\\keyboard_344.ckpt\n",
      "Epoch 345/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1553 - accuracy: 0.9460 - val_loss: 0.1562 - val_accuracy: 0.9448\n",
      "\n",
      "Epoch 00345: saving model to ckpt\\keyboard_345.ckpt\n",
      "Epoch 346/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1550 - accuracy: 0.9461 - val_loss: 0.1484 - val_accuracy: 0.9513\n",
      "\n",
      "Epoch 00346: saving model to ckpt\\keyboard_346.ckpt\n",
      "Epoch 347/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1563 - accuracy: 0.9457 - val_loss: 0.1631 - val_accuracy: 0.9467\n",
      "\n",
      "Epoch 00347: saving model to ckpt\\keyboard_347.ckpt\n",
      "Epoch 348/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1563 - accuracy: 0.9460 - val_loss: 0.1504 - val_accuracy: 0.9496\n",
      "\n",
      "Epoch 00348: saving model to ckpt\\keyboard_348.ckpt\n",
      "Epoch 349/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1527 - accuracy: 0.9474 - val_loss: 0.1495 - val_accuracy: 0.9511\n",
      "\n",
      "Epoch 00349: saving model to ckpt\\keyboard_349.ckpt\n",
      "Epoch 350/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1532 - accuracy: 0.9469 - val_loss: 0.1486 - val_accuracy: 0.9518\n",
      "\n",
      "Epoch 00350: saving model to ckpt\\keyboard_350.ckpt\n",
      "Epoch 351/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1558 - accuracy: 0.9456 - val_loss: 0.1507 - val_accuracy: 0.9494\n",
      "\n",
      "Epoch 00351: saving model to ckpt\\keyboard_351.ckpt\n",
      "Epoch 352/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1538 - accuracy: 0.9467 - val_loss: 0.1529 - val_accuracy: 0.9498\n",
      "\n",
      "Epoch 00352: saving model to ckpt\\keyboard_352.ckpt\n",
      "Epoch 353/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1533 - accuracy: 0.9466 - val_loss: 0.1588 - val_accuracy: 0.9453\n",
      "\n",
      "Epoch 00353: saving model to ckpt\\keyboard_353.ckpt\n",
      "Epoch 354/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1551 - accuracy: 0.9467 - val_loss: 0.1472 - val_accuracy: 0.9499\n",
      "\n",
      "Epoch 00354: saving model to ckpt\\keyboard_354.ckpt\n",
      "Epoch 355/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1522 - accuracy: 0.9474 - val_loss: 0.1519 - val_accuracy: 0.9491\n",
      "\n",
      "Epoch 00355: saving model to ckpt\\keyboard_355.ckpt\n",
      "Epoch 356/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1526 - accuracy: 0.9481 - val_loss: 0.1517 - val_accuracy: 0.9506\n",
      "\n",
      "Epoch 00356: saving model to ckpt\\keyboard_356.ckpt\n",
      "Epoch 357/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1559 - accuracy: 0.9458 - val_loss: 0.1487 - val_accuracy: 0.9499\n",
      "\n",
      "Epoch 00357: saving model to ckpt\\keyboard_357.ckpt\n",
      "Epoch 358/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1546 - accuracy: 0.9466 - val_loss: 0.1482 - val_accuracy: 0.9506\n",
      "\n",
      "Epoch 00358: saving model to ckpt\\keyboard_358.ckpt\n",
      "Epoch 359/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1544 - accuracy: 0.9474 - val_loss: 0.1507 - val_accuracy: 0.9489\n",
      "\n",
      "Epoch 00359: saving model to ckpt\\keyboard_359.ckpt\n",
      "Epoch 360/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1528 - accuracy: 0.9481 - val_loss: 0.1550 - val_accuracy: 0.9462\n",
      "\n",
      "Epoch 00360: saving model to ckpt\\keyboard_360.ckpt\n",
      "Epoch 361/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1541 - accuracy: 0.9468 - val_loss: 0.1528 - val_accuracy: 0.9505\n",
      "\n",
      "Epoch 00361: saving model to ckpt\\keyboard_361.ckpt\n",
      "Epoch 362/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1547 - accuracy: 0.9466 - val_loss: 0.1575 - val_accuracy: 0.9470\n",
      "\n",
      "Epoch 00362: saving model to ckpt\\keyboard_362.ckpt\n",
      "Epoch 363/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1538 - accuracy: 0.9469 - val_loss: 0.1570 - val_accuracy: 0.9477\n",
      "\n",
      "Epoch 00363: saving model to ckpt\\keyboard_363.ckpt\n",
      "Epoch 364/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1553 - accuracy: 0.9467 - val_loss: 0.1478 - val_accuracy: 0.9525\n",
      "\n",
      "Epoch 00364: saving model to ckpt\\keyboard_364.ckpt\n",
      "Epoch 365/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1533 - accuracy: 0.9470 - val_loss: 0.1524 - val_accuracy: 0.9486\n",
      "\n",
      "Epoch 00365: saving model to ckpt\\keyboard_365.ckpt\n",
      "Epoch 366/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1541 - accuracy: 0.9462 - val_loss: 0.1481 - val_accuracy: 0.9515\n",
      "\n",
      "Epoch 00366: saving model to ckpt\\keyboard_366.ckpt\n",
      "Epoch 367/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1527 - accuracy: 0.9470 - val_loss: 0.1562 - val_accuracy: 0.9498\n",
      "\n",
      "Epoch 00367: saving model to ckpt\\keyboard_367.ckpt\n",
      "Epoch 368/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1558 - accuracy: 0.9452 - val_loss: 0.1513 - val_accuracy: 0.9503\n",
      "\n",
      "Epoch 00368: saving model to ckpt\\keyboard_368.ckpt\n",
      "Epoch 369/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1535 - accuracy: 0.9482 - val_loss: 0.1470 - val_accuracy: 0.9518\n",
      "\n",
      "Epoch 00369: saving model to ckpt\\keyboard_369.ckpt\n",
      "Epoch 370/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1511 - accuracy: 0.9481 - val_loss: 0.1492 - val_accuracy: 0.9505\n",
      "\n",
      "Epoch 00370: saving model to ckpt\\keyboard_370.ckpt\n",
      "Epoch 371/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1528 - accuracy: 0.9471 - val_loss: 0.1505 - val_accuracy: 0.9506\n",
      "\n",
      "Epoch 00371: saving model to ckpt\\keyboard_371.ckpt\n",
      "Epoch 372/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1540 - accuracy: 0.9468 - val_loss: 0.1499 - val_accuracy: 0.9496\n",
      "\n",
      "Epoch 00372: saving model to ckpt\\keyboard_372.ckpt\n",
      "Epoch 373/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1556 - accuracy: 0.9453 - val_loss: 0.1530 - val_accuracy: 0.9493\n",
      "\n",
      "Epoch 00373: saving model to ckpt\\keyboard_373.ckpt\n",
      "Epoch 374/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1566 - accuracy: 0.9436 - val_loss: 0.1583 - val_accuracy: 0.9451\n",
      "\n",
      "Epoch 00374: saving model to ckpt\\keyboard_374.ckpt\n",
      "Epoch 375/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1537 - accuracy: 0.9463 - val_loss: 0.1522 - val_accuracy: 0.9486\n",
      "\n",
      "Epoch 00375: saving model to ckpt\\keyboard_375.ckpt\n",
      "Epoch 376/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1533 - accuracy: 0.9466 - val_loss: 0.1562 - val_accuracy: 0.9481\n",
      "\n",
      "Epoch 00376: saving model to ckpt\\keyboard_376.ckpt\n",
      "Epoch 377/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1550 - accuracy: 0.9466 - val_loss: 0.1484 - val_accuracy: 0.9503\n",
      "\n",
      "Epoch 00377: saving model to ckpt\\keyboard_377.ckpt\n",
      "Epoch 378/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1535 - accuracy: 0.9456 - val_loss: 0.1501 - val_accuracy: 0.9503\n",
      "\n",
      "Epoch 00378: saving model to ckpt\\keyboard_378.ckpt\n",
      "Epoch 379/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1523 - accuracy: 0.9473 - val_loss: 0.1578 - val_accuracy: 0.9465\n",
      "\n",
      "Epoch 00379: saving model to ckpt\\keyboard_379.ckpt\n",
      "Epoch 380/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1536 - accuracy: 0.9472 - val_loss: 0.1505 - val_accuracy: 0.9505\n",
      "\n",
      "Epoch 00380: saving model to ckpt\\keyboard_380.ckpt\n",
      "Epoch 381/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1539 - accuracy: 0.9463 - val_loss: 0.1511 - val_accuracy: 0.9505\n",
      "\n",
      "Epoch 00381: saving model to ckpt\\keyboard_381.ckpt\n",
      "Epoch 382/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1546 - accuracy: 0.9466 - val_loss: 0.1470 - val_accuracy: 0.9503\n",
      "\n",
      "Epoch 00382: saving model to ckpt\\keyboard_382.ckpt\n",
      "Epoch 383/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1530 - accuracy: 0.9465 - val_loss: 0.1561 - val_accuracy: 0.9443\n",
      "\n",
      "Epoch 00383: saving model to ckpt\\keyboard_383.ckpt\n",
      "Epoch 384/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1533 - accuracy: 0.9471 - val_loss: 0.1505 - val_accuracy: 0.9517\n",
      "\n",
      "Epoch 00384: saving model to ckpt\\keyboard_384.ckpt\n",
      "Epoch 385/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1524 - accuracy: 0.9474 - val_loss: 0.1486 - val_accuracy: 0.9501\n",
      "\n",
      "Epoch 00385: saving model to ckpt\\keyboard_385.ckpt\n",
      "Epoch 386/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1537 - accuracy: 0.9470 - val_loss: 0.1516 - val_accuracy: 0.9515\n",
      "\n",
      "Epoch 00386: saving model to ckpt\\keyboard_386.ckpt\n",
      "Epoch 387/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1511 - accuracy: 0.9481 - val_loss: 0.1515 - val_accuracy: 0.9487\n",
      "\n",
      "Epoch 00387: saving model to ckpt\\keyboard_387.ckpt\n",
      "Epoch 388/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1543 - accuracy: 0.9465 - val_loss: 0.1614 - val_accuracy: 0.9426\n",
      "\n",
      "Epoch 00388: saving model to ckpt\\keyboard_388.ckpt\n",
      "Epoch 389/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1551 - accuracy: 0.9449 - val_loss: 0.1520 - val_accuracy: 0.9498\n",
      "\n",
      "Epoch 00389: saving model to ckpt\\keyboard_389.ckpt\n",
      "Epoch 390/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1525 - accuracy: 0.9466 - val_loss: 0.1510 - val_accuracy: 0.9487\n",
      "\n",
      "Epoch 00390: saving model to ckpt\\keyboard_390.ckpt\n",
      "Epoch 391/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1544 - accuracy: 0.9464 - val_loss: 0.1481 - val_accuracy: 0.9515\n",
      "\n",
      "Epoch 00391: saving model to ckpt\\keyboard_391.ckpt\n",
      "Epoch 392/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1534 - accuracy: 0.9461 - val_loss: 0.1574 - val_accuracy: 0.9475\n",
      "\n",
      "Epoch 00392: saving model to ckpt\\keyboard_392.ckpt\n",
      "Epoch 393/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1539 - accuracy: 0.9464 - val_loss: 0.1481 - val_accuracy: 0.9515\n",
      "\n",
      "Epoch 00393: saving model to ckpt\\keyboard_393.ckpt\n",
      "Epoch 394/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1560 - accuracy: 0.9461 - val_loss: 0.1486 - val_accuracy: 0.9496\n",
      "\n",
      "Epoch 00394: saving model to ckpt\\keyboard_394.ckpt\n",
      "Epoch 395/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1526 - accuracy: 0.9465 - val_loss: 0.1521 - val_accuracy: 0.9513\n",
      "\n",
      "Epoch 00395: saving model to ckpt\\keyboard_395.ckpt\n",
      "Epoch 396/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1551 - accuracy: 0.9444 - val_loss: 0.1578 - val_accuracy: 0.9470\n",
      "\n",
      "Epoch 00396: saving model to ckpt\\keyboard_396.ckpt\n",
      "Epoch 397/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1548 - accuracy: 0.9459 - val_loss: 0.1564 - val_accuracy: 0.9451\n",
      "\n",
      "Epoch 00397: saving model to ckpt\\keyboard_397.ckpt\n",
      "Epoch 398/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1539 - accuracy: 0.9461 - val_loss: 0.1450 - val_accuracy: 0.9520\n",
      "\n",
      "Epoch 00398: saving model to ckpt\\keyboard_398.ckpt\n",
      "Epoch 399/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1508 - accuracy: 0.9478 - val_loss: 0.1487 - val_accuracy: 0.9503\n",
      "\n",
      "Epoch 00399: saving model to ckpt\\keyboard_399.ckpt\n",
      "Epoch 400/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1544 - accuracy: 0.9467 - val_loss: 0.1510 - val_accuracy: 0.9508\n",
      "\n",
      "Epoch 00400: saving model to ckpt\\keyboard_400.ckpt\n",
      "Epoch 401/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1521 - accuracy: 0.9474 - val_loss: 0.1465 - val_accuracy: 0.9522\n",
      "\n",
      "Epoch 00401: saving model to ckpt\\keyboard_401.ckpt\n",
      "Epoch 402/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1532 - accuracy: 0.9462 - val_loss: 0.1482 - val_accuracy: 0.9520\n",
      "\n",
      "Epoch 00402: saving model to ckpt\\keyboard_402.ckpt\n",
      "Epoch 403/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1519 - accuracy: 0.9474 - val_loss: 0.1546 - val_accuracy: 0.9477\n",
      "\n",
      "Epoch 00403: saving model to ckpt\\keyboard_403.ckpt\n",
      "Epoch 404/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1536 - accuracy: 0.9461 - val_loss: 0.1460 - val_accuracy: 0.9520\n",
      "\n",
      "Epoch 00404: saving model to ckpt\\keyboard_404.ckpt\n",
      "Epoch 405/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1540 - accuracy: 0.9466 - val_loss: 0.1601 - val_accuracy: 0.9422\n",
      "\n",
      "Epoch 00405: saving model to ckpt\\keyboard_405.ckpt\n",
      "Epoch 406/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1519 - accuracy: 0.9468 - val_loss: 0.1528 - val_accuracy: 0.9486\n",
      "\n",
      "Epoch 00406: saving model to ckpt\\keyboard_406.ckpt\n",
      "Epoch 407/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1519 - accuracy: 0.9476 - val_loss: 0.1535 - val_accuracy: 0.9474\n",
      "\n",
      "Epoch 00407: saving model to ckpt\\keyboard_407.ckpt\n",
      "Epoch 408/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1541 - accuracy: 0.9465 - val_loss: 0.1479 - val_accuracy: 0.9501\n",
      "\n",
      "Epoch 00408: saving model to ckpt\\keyboard_408.ckpt\n",
      "Epoch 409/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1520 - accuracy: 0.9475 - val_loss: 0.1589 - val_accuracy: 0.9469\n",
      "\n",
      "Epoch 00409: saving model to ckpt\\keyboard_409.ckpt\n",
      "Epoch 410/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1526 - accuracy: 0.9470 - val_loss: 0.1471 - val_accuracy: 0.9493\n",
      "\n",
      "Epoch 00410: saving model to ckpt\\keyboard_410.ckpt\n",
      "Epoch 411/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1531 - accuracy: 0.9462 - val_loss: 0.1481 - val_accuracy: 0.9508\n",
      "\n",
      "Epoch 00411: saving model to ckpt\\keyboard_411.ckpt\n",
      "Epoch 412/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1515 - accuracy: 0.9472 - val_loss: 0.1461 - val_accuracy: 0.9529\n",
      "\n",
      "Epoch 00412: saving model to ckpt\\keyboard_412.ckpt\n",
      "Epoch 413/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1512 - accuracy: 0.9479 - val_loss: 0.1485 - val_accuracy: 0.9501\n",
      "\n",
      "Epoch 00413: saving model to ckpt\\keyboard_413.ckpt\n",
      "Epoch 414/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1521 - accuracy: 0.9469 - val_loss: 0.1570 - val_accuracy: 0.9472\n",
      "\n",
      "Epoch 00414: saving model to ckpt\\keyboard_414.ckpt\n",
      "Epoch 415/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1524 - accuracy: 0.9475 - val_loss: 0.1501 - val_accuracy: 0.9498\n",
      "\n",
      "Epoch 00415: saving model to ckpt\\keyboard_415.ckpt\n",
      "Epoch 416/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1511 - accuracy: 0.9482 - val_loss: 0.1498 - val_accuracy: 0.9503\n",
      "\n",
      "Epoch 00416: saving model to ckpt\\keyboard_416.ckpt\n",
      "Epoch 417/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1514 - accuracy: 0.9466 - val_loss: 0.1507 - val_accuracy: 0.9506\n",
      "\n",
      "Epoch 00417: saving model to ckpt\\keyboard_417.ckpt\n",
      "Epoch 418/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1515 - accuracy: 0.9481 - val_loss: 0.1588 - val_accuracy: 0.9439\n",
      "\n",
      "Epoch 00418: saving model to ckpt\\keyboard_418.ckpt\n",
      "Epoch 419/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1566 - accuracy: 0.9460 - val_loss: 0.1498 - val_accuracy: 0.9515\n",
      "\n",
      "Epoch 00419: saving model to ckpt\\keyboard_419.ckpt\n",
      "Epoch 420/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1550 - accuracy: 0.9467 - val_loss: 0.1575 - val_accuracy: 0.9499\n",
      "\n",
      "Epoch 00420: saving model to ckpt\\keyboard_420.ckpt\n",
      "Epoch 421/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1523 - accuracy: 0.9471 - val_loss: 0.1537 - val_accuracy: 0.9462\n",
      "\n",
      "Epoch 00421: saving model to ckpt\\keyboard_421.ckpt\n",
      "Epoch 422/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1534 - accuracy: 0.9469 - val_loss: 0.1484 - val_accuracy: 0.9511\n",
      "\n",
      "Epoch 00422: saving model to ckpt\\keyboard_422.ckpt\n",
      "Epoch 423/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1509 - accuracy: 0.9485 - val_loss: 0.1519 - val_accuracy: 0.9494\n",
      "\n",
      "Epoch 00423: saving model to ckpt\\keyboard_423.ckpt\n",
      "Epoch 424/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1531 - accuracy: 0.9473 - val_loss: 0.1608 - val_accuracy: 0.9457\n",
      "\n",
      "Epoch 00424: saving model to ckpt\\keyboard_424.ckpt\n",
      "Epoch 425/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1522 - accuracy: 0.9467 - val_loss: 0.1505 - val_accuracy: 0.9477\n",
      "\n",
      "Epoch 00425: saving model to ckpt\\keyboard_425.ckpt\n",
      "Epoch 426/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1529 - accuracy: 0.9479 - val_loss: 0.1525 - val_accuracy: 0.9481\n",
      "\n",
      "Epoch 00426: saving model to ckpt\\keyboard_426.ckpt\n",
      "Epoch 427/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1529 - accuracy: 0.9460 - val_loss: 0.1593 - val_accuracy: 0.9446\n",
      "\n",
      "Epoch 00427: saving model to ckpt\\keyboard_427.ckpt\n",
      "Epoch 428/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1533 - accuracy: 0.9462 - val_loss: 0.1473 - val_accuracy: 0.9506\n",
      "\n",
      "Epoch 00428: saving model to ckpt\\keyboard_428.ckpt\n",
      "Epoch 429/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1526 - accuracy: 0.9472 - val_loss: 0.1502 - val_accuracy: 0.9505\n",
      "\n",
      "Epoch 00429: saving model to ckpt\\keyboard_429.ckpt\n",
      "Epoch 430/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1522 - accuracy: 0.9473 - val_loss: 0.1495 - val_accuracy: 0.9499\n",
      "\n",
      "Epoch 00430: saving model to ckpt\\keyboard_430.ckpt\n",
      "Epoch 431/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1524 - accuracy: 0.9472 - val_loss: 0.1632 - val_accuracy: 0.9409\n",
      "\n",
      "Epoch 00431: saving model to ckpt\\keyboard_431.ckpt\n",
      "Epoch 432/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1554 - accuracy: 0.9442 - val_loss: 0.1518 - val_accuracy: 0.9482\n",
      "\n",
      "Epoch 00432: saving model to ckpt\\keyboard_432.ckpt\n",
      "Epoch 433/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1540 - accuracy: 0.9466 - val_loss: 0.1478 - val_accuracy: 0.9499\n",
      "\n",
      "Epoch 00433: saving model to ckpt\\keyboard_433.ckpt\n",
      "Epoch 434/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1538 - accuracy: 0.9465 - val_loss: 0.1457 - val_accuracy: 0.9517\n",
      "\n",
      "Epoch 00434: saving model to ckpt\\keyboard_434.ckpt\n",
      "Epoch 435/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1514 - accuracy: 0.9470 - val_loss: 0.1482 - val_accuracy: 0.9522\n",
      "\n",
      "Epoch 00435: saving model to ckpt\\keyboard_435.ckpt\n",
      "Epoch 436/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1517 - accuracy: 0.9475 - val_loss: 0.1481 - val_accuracy: 0.9505\n",
      "\n",
      "Epoch 00436: saving model to ckpt\\keyboard_436.ckpt\n",
      "Epoch 437/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1513 - accuracy: 0.9477 - val_loss: 0.1570 - val_accuracy: 0.9463\n",
      "\n",
      "Epoch 00437: saving model to ckpt\\keyboard_437.ckpt\n",
      "Epoch 438/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1527 - accuracy: 0.9472 - val_loss: 0.1475 - val_accuracy: 0.9511\n",
      "\n",
      "Epoch 00438: saving model to ckpt\\keyboard_438.ckpt\n",
      "Epoch 439/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1499 - accuracy: 0.9477 - val_loss: 0.1495 - val_accuracy: 0.9508\n",
      "\n",
      "Epoch 00439: saving model to ckpt\\keyboard_439.ckpt\n",
      "Epoch 440/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1521 - accuracy: 0.9476 - val_loss: 0.1501 - val_accuracy: 0.9493\n",
      "\n",
      "Epoch 00440: saving model to ckpt\\keyboard_440.ckpt\n",
      "Epoch 441/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1527 - accuracy: 0.9469 - val_loss: 0.1481 - val_accuracy: 0.9510\n",
      "\n",
      "Epoch 00441: saving model to ckpt\\keyboard_441.ckpt\n",
      "Epoch 442/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1509 - accuracy: 0.9473 - val_loss: 0.1522 - val_accuracy: 0.9498\n",
      "\n",
      "Epoch 00442: saving model to ckpt\\keyboard_442.ckpt\n",
      "Epoch 443/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1515 - accuracy: 0.9476 - val_loss: 0.1460 - val_accuracy: 0.9520\n",
      "\n",
      "Epoch 00443: saving model to ckpt\\keyboard_443.ckpt\n",
      "Epoch 444/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1536 - accuracy: 0.9467 - val_loss: 0.1474 - val_accuracy: 0.9510\n",
      "\n",
      "Epoch 00444: saving model to ckpt\\keyboard_444.ckpt\n",
      "Epoch 445/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1517 - accuracy: 0.9468 - val_loss: 0.1497 - val_accuracy: 0.9494\n",
      "\n",
      "Epoch 00445: saving model to ckpt\\keyboard_445.ckpt\n",
      "Epoch 446/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1516 - accuracy: 0.9476 - val_loss: 0.1496 - val_accuracy: 0.9529\n",
      "\n",
      "Epoch 00446: saving model to ckpt\\keyboard_446.ckpt\n",
      "Epoch 447/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1520 - accuracy: 0.9472 - val_loss: 0.1466 - val_accuracy: 0.9520\n",
      "\n",
      "Epoch 00447: saving model to ckpt\\keyboard_447.ckpt\n",
      "Epoch 448/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1525 - accuracy: 0.9466 - val_loss: 0.1521 - val_accuracy: 0.9484\n",
      "\n",
      "Epoch 00448: saving model to ckpt\\keyboard_448.ckpt\n",
      "Epoch 449/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1521 - accuracy: 0.9467 - val_loss: 0.1519 - val_accuracy: 0.9503\n",
      "\n",
      "Epoch 00449: saving model to ckpt\\keyboard_449.ckpt\n",
      "Epoch 450/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1524 - accuracy: 0.9471 - val_loss: 0.1509 - val_accuracy: 0.9498\n",
      "\n",
      "Epoch 00450: saving model to ckpt\\keyboard_450.ckpt\n",
      "Epoch 451/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1516 - accuracy: 0.9478 - val_loss: 0.1562 - val_accuracy: 0.9448\n",
      "\n",
      "Epoch 00451: saving model to ckpt\\keyboard_451.ckpt\n",
      "Epoch 452/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1528 - accuracy: 0.9477 - val_loss: 0.1479 - val_accuracy: 0.9501\n",
      "\n",
      "Epoch 00452: saving model to ckpt\\keyboard_452.ckpt\n",
      "Epoch 453/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1536 - accuracy: 0.9459 - val_loss: 0.1522 - val_accuracy: 0.9477\n",
      "\n",
      "Epoch 00453: saving model to ckpt\\keyboard_453.ckpt\n",
      "Epoch 454/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1525 - accuracy: 0.9465 - val_loss: 0.1567 - val_accuracy: 0.9446\n",
      "\n",
      "Epoch 00454: saving model to ckpt\\keyboard_454.ckpt\n",
      "Epoch 455/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1530 - accuracy: 0.9463 - val_loss: 0.1487 - val_accuracy: 0.9506\n",
      "\n",
      "Epoch 00455: saving model to ckpt\\keyboard_455.ckpt\n",
      "Epoch 456/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1518 - accuracy: 0.9477 - val_loss: 0.1515 - val_accuracy: 0.9510\n",
      "\n",
      "Epoch 00456: saving model to ckpt\\keyboard_456.ckpt\n",
      "Epoch 457/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1504 - accuracy: 0.9478 - val_loss: 0.1451 - val_accuracy: 0.9515\n",
      "\n",
      "Epoch 00457: saving model to ckpt\\keyboard_457.ckpt\n",
      "Epoch 458/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1495 - accuracy: 0.9490 - val_loss: 0.1495 - val_accuracy: 0.9511\n",
      "\n",
      "Epoch 00458: saving model to ckpt\\keyboard_458.ckpt\n",
      "Epoch 459/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1530 - accuracy: 0.9468 - val_loss: 0.1458 - val_accuracy: 0.9518\n",
      "\n",
      "Epoch 00459: saving model to ckpt\\keyboard_459.ckpt\n",
      "Epoch 460/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1555 - accuracy: 0.9450 - val_loss: 0.1494 - val_accuracy: 0.9499\n",
      "\n",
      "Epoch 00460: saving model to ckpt\\keyboard_460.ckpt\n",
      "Epoch 461/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1513 - accuracy: 0.9476 - val_loss: 0.1479 - val_accuracy: 0.9498\n",
      "\n",
      "Epoch 00461: saving model to ckpt\\keyboard_461.ckpt\n",
      "Epoch 462/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1508 - accuracy: 0.9481 - val_loss: 0.1451 - val_accuracy: 0.9535\n",
      "\n",
      "Epoch 00462: saving model to ckpt\\keyboard_462.ckpt\n",
      "Epoch 463/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1506 - accuracy: 0.9476 - val_loss: 0.1480 - val_accuracy: 0.9513\n",
      "\n",
      "Epoch 00463: saving model to ckpt\\keyboard_463.ckpt\n",
      "Epoch 464/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1507 - accuracy: 0.9481 - val_loss: 0.1473 - val_accuracy: 0.9508\n",
      "\n",
      "Epoch 00464: saving model to ckpt\\keyboard_464.ckpt\n",
      "Epoch 465/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1521 - accuracy: 0.9474 - val_loss: 0.1574 - val_accuracy: 0.9455\n",
      "\n",
      "Epoch 00465: saving model to ckpt\\keyboard_465.ckpt\n",
      "Epoch 466/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1531 - accuracy: 0.9475 - val_loss: 0.1482 - val_accuracy: 0.9517\n",
      "\n",
      "Epoch 00466: saving model to ckpt\\keyboard_466.ckpt\n",
      "Epoch 467/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1498 - accuracy: 0.9476 - val_loss: 0.1480 - val_accuracy: 0.9506\n",
      "\n",
      "Epoch 00467: saving model to ckpt\\keyboard_467.ckpt\n",
      "Epoch 468/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1498 - accuracy: 0.9481 - val_loss: 0.1447 - val_accuracy: 0.9506\n",
      "\n",
      "Epoch 00468: saving model to ckpt\\keyboard_468.ckpt\n",
      "Epoch 469/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1527 - accuracy: 0.9480 - val_loss: 0.1487 - val_accuracy: 0.9515\n",
      "\n",
      "Epoch 00469: saving model to ckpt\\keyboard_469.ckpt\n",
      "Epoch 470/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1529 - accuracy: 0.9462 - val_loss: 0.1531 - val_accuracy: 0.9479\n",
      "\n",
      "Epoch 00470: saving model to ckpt\\keyboard_470.ckpt\n",
      "Epoch 471/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1517 - accuracy: 0.9466 - val_loss: 0.1519 - val_accuracy: 0.9487\n",
      "\n",
      "Epoch 00471: saving model to ckpt\\keyboard_471.ckpt\n",
      "Epoch 472/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1537 - accuracy: 0.9460 - val_loss: 0.1558 - val_accuracy: 0.9465\n",
      "\n",
      "Epoch 00472: saving model to ckpt\\keyboard_472.ckpt\n",
      "Epoch 473/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1533 - accuracy: 0.9464 - val_loss: 0.1470 - val_accuracy: 0.9501\n",
      "\n",
      "Epoch 00473: saving model to ckpt\\keyboard_473.ckpt\n",
      "Epoch 474/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1519 - accuracy: 0.9469 - val_loss: 0.1478 - val_accuracy: 0.9515\n",
      "\n",
      "Epoch 00474: saving model to ckpt\\keyboard_474.ckpt\n",
      "Epoch 475/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1507 - accuracy: 0.9481 - val_loss: 0.1446 - val_accuracy: 0.9534\n",
      "\n",
      "Epoch 00475: saving model to ckpt\\keyboard_475.ckpt\n",
      "Epoch 476/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1517 - accuracy: 0.9475 - val_loss: 0.1500 - val_accuracy: 0.9491\n",
      "\n",
      "Epoch 00476: saving model to ckpt\\keyboard_476.ckpt\n",
      "Epoch 477/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1517 - accuracy: 0.9472 - val_loss: 0.1516 - val_accuracy: 0.9489\n",
      "\n",
      "Epoch 00477: saving model to ckpt\\keyboard_477.ckpt\n",
      "Epoch 478/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1507 - accuracy: 0.9474 - val_loss: 0.1472 - val_accuracy: 0.9498\n",
      "\n",
      "Epoch 00478: saving model to ckpt\\keyboard_478.ckpt\n",
      "Epoch 479/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1500 - accuracy: 0.9483 - val_loss: 0.1514 - val_accuracy: 0.9499\n",
      "\n",
      "Epoch 00479: saving model to ckpt\\keyboard_479.ckpt\n",
      "Epoch 480/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1507 - accuracy: 0.9465 - val_loss: 0.1502 - val_accuracy: 0.9489\n",
      "\n",
      "Epoch 00480: saving model to ckpt\\keyboard_480.ckpt\n",
      "Epoch 481/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1517 - accuracy: 0.9469 - val_loss: 0.1513 - val_accuracy: 0.9479\n",
      "\n",
      "Epoch 00481: saving model to ckpt\\keyboard_481.ckpt\n",
      "Epoch 482/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1511 - accuracy: 0.9473 - val_loss: 0.1512 - val_accuracy: 0.9491\n",
      "\n",
      "Epoch 00482: saving model to ckpt\\keyboard_482.ckpt\n",
      "Epoch 483/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1522 - accuracy: 0.9475 - val_loss: 0.1470 - val_accuracy: 0.9508\n",
      "\n",
      "Epoch 00483: saving model to ckpt\\keyboard_483.ckpt\n",
      "Epoch 484/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1508 - accuracy: 0.9485 - val_loss: 0.1464 - val_accuracy: 0.9503\n",
      "\n",
      "Epoch 00484: saving model to ckpt\\keyboard_484.ckpt\n",
      "Epoch 485/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1516 - accuracy: 0.9478 - val_loss: 0.1536 - val_accuracy: 0.9465\n",
      "\n",
      "Epoch 00485: saving model to ckpt\\keyboard_485.ckpt\n",
      "Epoch 486/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1498 - accuracy: 0.9481 - val_loss: 0.1517 - val_accuracy: 0.9508\n",
      "\n",
      "Epoch 00486: saving model to ckpt\\keyboard_486.ckpt\n",
      "Epoch 487/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1544 - accuracy: 0.9460 - val_loss: 0.1494 - val_accuracy: 0.9493\n",
      "\n",
      "Epoch 00487: saving model to ckpt\\keyboard_487.ckpt\n",
      "Epoch 488/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1510 - accuracy: 0.9481 - val_loss: 0.1501 - val_accuracy: 0.9506\n",
      "\n",
      "Epoch 00488: saving model to ckpt\\keyboard_488.ckpt\n",
      "Epoch 489/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1519 - accuracy: 0.9472 - val_loss: 0.1470 - val_accuracy: 0.9508\n",
      "\n",
      "Epoch 00489: saving model to ckpt\\keyboard_489.ckpt\n",
      "Epoch 490/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1499 - accuracy: 0.9477 - val_loss: 0.1428 - val_accuracy: 0.9527\n",
      "\n",
      "Epoch 00490: saving model to ckpt\\keyboard_490.ckpt\n",
      "Epoch 491/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1500 - accuracy: 0.9478 - val_loss: 0.1530 - val_accuracy: 0.9469\n",
      "\n",
      "Epoch 00491: saving model to ckpt\\keyboard_491.ckpt\n",
      "Epoch 492/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1520 - accuracy: 0.9465 - val_loss: 0.1632 - val_accuracy: 0.9434\n",
      "\n",
      "Epoch 00492: saving model to ckpt\\keyboard_492.ckpt\n",
      "Epoch 493/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1547 - accuracy: 0.9452 - val_loss: 0.1550 - val_accuracy: 0.9491\n",
      "\n",
      "Epoch 00493: saving model to ckpt\\keyboard_493.ckpt\n",
      "Epoch 494/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1523 - accuracy: 0.9459 - val_loss: 0.1503 - val_accuracy: 0.9498\n",
      "\n",
      "Epoch 00494: saving model to ckpt\\keyboard_494.ckpt\n",
      "Epoch 495/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1537 - accuracy: 0.9460 - val_loss: 0.1507 - val_accuracy: 0.9496\n",
      "\n",
      "Epoch 00495: saving model to ckpt\\keyboard_495.ckpt\n",
      "Epoch 496/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1515 - accuracy: 0.9475 - val_loss: 0.1467 - val_accuracy: 0.9520\n",
      "\n",
      "Epoch 00496: saving model to ckpt\\keyboard_496.ckpt\n",
      "Epoch 497/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1505 - accuracy: 0.9478 - val_loss: 0.1529 - val_accuracy: 0.9484\n",
      "\n",
      "Epoch 00497: saving model to ckpt\\keyboard_497.ckpt\n",
      "Epoch 498/500\n",
      "183/183 [==============================] - 1s 5ms/step - loss: 0.1492 - accuracy: 0.9485 - val_loss: 0.1492 - val_accuracy: 0.9498\n",
      "\n",
      "Epoch 00498: saving model to ckpt\\keyboard_498.ckpt\n",
      "Epoch 499/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1525 - accuracy: 0.9463 - val_loss: 0.1470 - val_accuracy: 0.9523\n",
      "\n",
      "Epoch 00499: saving model to ckpt\\keyboard_499.ckpt\n",
      "Epoch 500/500\n",
      "183/183 [==============================] - 1s 4ms/step - loss: 0.1513 - accuracy: 0.9467 - val_loss: 0.1477 - val_accuracy: 0.9511\n",
      "\n",
      "Epoch 00500: saving model to ckpt\\keyboard_500.ckpt\n"
     ]
    }
   ],
   "source": [
    "hist = model.fit(X_train, y_train, validation_data=(X_val, y_val), batch_size=128, validation_batch_size=128, epochs=500, callbacks=[ckpt_callback])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_model(model, to_file='model_archtecture.png', show_shapes=True)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. 모델 학습 결과 출력"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_vloss = hist.history['val_loss']\n",
    "y_loss = hist.history['loss']\n",
    "\n",
    "x_len = np.arange(len(y_loss))\n",
    "plt.plot(x_len, y_vloss, marker='.', c='red', label=\"Validation-set Loss\")\n",
    "plt.plot(x_len, y_loss, marker='.', c='blue', label=\"Train-set Loss\")\n",
    "\n",
    "plt.legend(loc='upper right')\n",
    "plt.grid()\n",
    "plt.xlabel('epoch')\n",
    "plt.ylabel('loss')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_vacc = hist.history['val_accuracy']\n",
    "y_acc = hist.history['accuracy']\n",
    "\n",
    "x_len = np.arange(len(y_acc))\n",
    "plt.plot(x_len, y_vacc, marker='.', c='red', label=\"Validation-set Accuracy\")\n",
    "plt.plot(x_len, y_acc, marker='.', c='blue', label=\"Train-set Accuracy\")\n",
    "\n",
    "plt.legend(loc='lower right')\n",
    "plt.grid()\n",
    "plt.xlabel('epoch')\n",
    "plt.ylabel('accuracy')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: demo_keyboard_model\\assets\n"
     ]
    }
   ],
   "source": [
    "model.save(\"demo_keyboard_model\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6. 학습한 모델로 가상 인터페이스 생성 테스트"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 테스트 데이터 생성\n",
    "test_data = pd.DataFrame(columns=['x','y'])\n",
    "for x in range(1280):\n",
    "    for y in range(960):\n",
    "        temp = pd.DataFrame({'x':[x], 'y':[y]})\n",
    "\n",
    "        test_data = test_data.append(temp, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 미리 생성한 테스트 데이터 있을 경우 불러올 수 있음\n",
    "test_data = pd.read_hdf('1280x960.h5', 'df')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\KYH\\.conda\\envs\\tensor\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\sequential.py:455: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype(\"int32\")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).\n",
      "  warnings.warn('`model.predict_classes()` is deprecated and '\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_input = tf.convert_to_tensor(test_data, dtype=tf.float64)\n",
    "pred = model.predict_classes(test_input)\n",
    "pred = tf.reshape(pred, [1280, 960]).numpy()\n",
    "\n",
    "test = np.full((960, 1280, 3), 255, np.uint8)\n",
    "for y in range(960):\n",
    "    for x in range(1280):\n",
    "        test[y][x] = colors[pred[x][y]]\n",
    "\n",
    "plt.imshow(test)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = load_model(\"demo_keyboard_model\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 7. Epoch 별 모델이 생성한 가상 인터페이스 결과"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch : 1\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch : 10\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch : 20\n"
     ]
    },
    {
     "data": {
      "image/png": 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AM10m97UdhD8axpzpw6Q6viRvbUsEzOQhN2Qo3RuSHw0Ieg1m6aDV7d6N2SydDhhiYBgKOx0QQgI2hrRqieccyawlmXWki5u3Zivx+t3v4ra1b26gZIC8w22LSLbNgIP4facw1QLhCyPkvrSb3NfHMXO6e7isblMHzBSguCek5/6QcMBku4jrdGhs5XHzEOYh7AnJ78ii5jrQeiWldTTBrn5HMS85HOlP3GK8rmdpLt4NRyQ/dZ7kHecxZ3op/M97yH1pN8GJiu4oLm+yKQNmilC6J6S0PyQ3aDDBxrzwl6NmStDzcEhxd0DzaEp0OsW2NmST1tSaxetaBgjB7WnQ+TsvE/3118j/yW5yX9xDeHQA2qFGZQJstoAFUNwV0PtIjnDw5m4Rf7cYk825VX7ckD4Y0vxhSueNFJds9JbdHofD3rtI6188i9u+hvG6lgE30iH61WNEH3qd8MURCn8wRe6bY1DPK2Rb3KYJWG7Y0PtIjsJEgMl174vaGENuwFA5bChNhbRfTWm/kXp1JNPhcJNNWp/5Dm6yeXcODhqgZEl/bIbWY5cIXxih+K8PEn57FGM14b9V+f/Mh1A+EDL4rgKFXd0dryuZwFAYDag8lWPwp/Pkxz16Kvpj2r/1feyh+Y1Z2ZBzpI9dovnvvkXnH72Eq0QbsBFry63yn9yY1yOwoAyVp/LZqGuD5rnulAkM+R0BAyMB1T+LSC5198vW4Yh+4QTJT17Y2GVZBuhNiD78CnZHk/LHn8A0/Tkf0gGEBrejRPrUCOnDA7hdV1/p1pxrE5xpERxvwHxEcLEDtRizmECSLQj281W/drwNmClB/9N58juCrprruh3GGCg4SveE1C91+aRY4Eifutg9Y/cAkvecpt3MUfpnb/ciYg5w+3rpfPwA6Tu347YVYbU/wM5lZy44B/UE07aY001MPSF4uUZwtoU52QQgONXE1BLMpQ5EFuLsj6Hf744b8zZghfFgU8RrmTGGoMeDn6U3Id1X2+ituFoI8QdOYKoFir/zMCbplrq+mQsgec84nX/1MG6y9NZnvBsDIYCBwUIWv/Hswo3pT41y1WVA2hZiS3ChjZmNMCcamIWY4JVFghMNzExEcK4FrRQ6Fpz/cfM2YEHR91+9n+xIB7ets9Gb8WYhRL9yDDNdpvAHU125kt/1hnQ+doD4w3uhskZvvSsDWA6hHGL7l0ahh0eWvrGDxEHsMHMRZqadxexCm+ClBYIfLmSBm4sgcl5FzduAhYM+/ZrfmnMO2+ru+S9gacFpl25n0dL5By9h4oD8H97XfQtfCwFuVw+U7nJcjYG8gTy4njJuZzbXlkIWt9hhFmLMySbhC1XCZ+cJflTDnGlhGgl0bNdGzduAwfqtqr/bnHPEM47mD7t8/gtwkw0o2I3ejNWVLO3fOII520vuqzu6a53YfEzpbz9H8tWddP7JA7jx4vpdNO1mGQMFg9texG0vYh8bJP7wPVm0ZjqYC23CZ+cJX1ogeL5KcLoJzbRr/oZ5HbDNwDlHMuuofSP2Y3V+F/VgVZWY9j99jp43fpLgeKVrImYAOpbcH54ieGmBziceIH3ndsh30e7uclBLIW5XD25XD/aJYWLroJESnGoQvFInOLJA8OoiwckmwRtNqCcbMqemgG2g5Xgt/L8YW++SP2lvIXi1HzNfwG3vwnmwZQbc7gbtTz5Hz998Gtrd9TI3QPDSAuVf+h7RR/YR/d0p6M9t/GjsRgIDlRz24AD24AC8fyL7eMtizjYJj9QIn68SPDtH8EYTM9OB1K171Lrrmb0FLsoC4OtupI/xAjAzZXLfHCf+qye7ezRmIH1ymvjnTpH/o71dMwpbtjwaK/zeMcJnZon+8QOkT490d8SutLydPSFuqkIyVSF5/wQk2YGC4FgdM90heGmB8MUqwWuNLGrttd399DZg8UULh8LufhNdh3PZX6Vk1rHwdb/iBYCFwuemiN91Fvq6fM4u74h+KTsRvNtGYcuMhfCZOUq/+izRb95P/MFd0Bv6E7IrLR0scGMl0rGle3S+fyJbx7YYZ4tyjy5mUfve0kitFmfLOm5zUW53Pqs3Ibpo6Zy2FHf7sxbMOYdtQOPFhM6pFOfhGTAGQ/DyEMX/eIDOr7/cPQtaV2Hvq2GnaoRHhjd6U1ZlAHMpovixI+S+eJ7Opw9hD1T8jNi1ltexDRawgwXsoQH4wGR25LMawUJCcGyR8Pns6Kc52yI43brpkZq3ASOFxnMJ+eE8YaX7nmi3vMDQQtpwJHOO+IKlcybFNjZ22+6UsQH5z0+RvOMC6dtmu3sUXEhxQ108X3cFkzrCr81Qft8zxH/jHuJf3o2buInFrr5ZPvI5WoJRSKf6SN8zno3UmgnBG02Co4vk/mya4KXqjR/K3eCGfp/p/UjX798UdgX0P50nKGzsk3xlsGyUXZG1c9KSzFuSqvNytHUjDod9aI7mZ7+FG291bcTM2R563/8zBJfKb/3FXcQBbleZ+Ff2EP3aXhjMb76Q3cjy+6mW8ImR9676g/s7AlsSnbY0nk/oezyHCe/OE7w8j+VisE1HfMmSNhxpzWHrjrTusiuvdn3+b5/BEBwZpvzRp+h8/EXSh+e6Z32YA9ohuWdGKXx+CnOptNFbdMsMYE63KPz2UXL/6zzxB3eS/PwkbscmHJFdz/LPOHDjc1u9H4EBEEDvIzl6DoZrGrGVUKXZUU/bctmIajb7f9rM7k5El89lryeHg56U5Mlp4l88QfrYJdxgBPl1vlTC8iszCiAOMJ0QM10ieL2f4PV+8l+dIDg6kH2uW4eHt8ABbneZ6O/vJ37fRPbGDtgSMftE7mdX/SE3R8Bg5WqsPQ/lyA2ZpTtD3/yT65bO+rcR2EVHspjdZSiZz0ZVNl7aDeySQUa3cTgIwG1rY3c2SB+9RPr4Jey9NexEE0rp6hP+juwGtVe+2lKDaeSWblybMXNFzHwRU88TvF4hOFEheHWAYLYIUYiZL2ZBc6vfc9JnDrLf8c4y6ZPDJO/dQfrUCG70Blez2AS2RsCWmDzkBrMrO4SDhlx/ACHkKubNO8wppLVsly+pZaMqu+iwCtUdWw4aPQl2Vx17X430wSp2Tx0zXyA4UbnyiwlfG4Dm5SfIdALMdBniy9UzrRA6YXafSLs5I3UrXJBdmSJ51xjp0yPYtw1id/dAMdhUI7MtFbBVXWdpjXN4dSln37lrJgW3eoDWioOlCzzmsA9USA+PkLx7jPRgP/TnlybU/P1d3yhg3k/i37R0U8+pe0HBWh8Gshd3PcmuJPHsPPnPHsdNlkgfGcyi9sgg9lA/bqyUXZnC46BdaesETGSLMACRxZxoEpxo4r5ANnc2WMDu7yP9sWHSwyNZ0IYL2XXEPB2lKWAim5yBbM5wLiL4zhzhd+bg37yGGyrgthWwDw+S/sQI6aF+7P4K9OW8OcKpgIlsMStBm41gNiJ8pU7uj89AIcDu6cFO9ZG+axT7YD92T082Sit054EBBUxEVnY7w2N1gmN1cn96IbuC7GAet7uH9Ikh0sNLd0/aUeqaI50KmIhcZSVLkcVMd2C6Q/j9edy/Pw79eey+XtLHh7D39pE+MYTd15vtdubu/pn9CpiI3BRjgWpM+IMq4Q+q2VH9YoCbKGHv6yP5i6PYhway5RulMLv2/zqP0hQwEbktyxdlXD7aGf6f6Wy3c6yIGy6QvHM79uEB0kcGcZPlLGhrfMaAAiYia2Jl+cbpFpxuEb64gDNAXw57Tw/2gf7sLuSPDuIGC7ixYjaXBrc9UlPARGTdGAcsJoRHagRHauT+6Ew2EiuF2Pv6SB/sJz08jH37IHainN0v8xbWpClgInJXrCSpbaFtCb8/nx0c+PxJ6A1xoyXShwewU33Y/RXcQB43UoAfX/0xFTAR2VDGAfUUU28QHG9cPuXPkK0/m/trq/5bBUxEusrKSM0BnRtfFqbLb8kgIrI6BUxEvKWAiYi3FDAR8ZYCJiLeUsBExFsKmIh4SwETEW8pYCLiLQVMRLylgImItxQwEfGWAiYi3lLARMRbCpiIeEsBExFvKWAi4i0FTES8pYCJiLcUMBHxlgImIt5SwETEWwqYiHhLARMRbylgIuItBUxEvKWAiYi3FDAR8ZYCJiLeUsBExFsKmIh4SwETEW8pYCLiLQVMRLylgImItxQwEfGWAiYi3lLARMRbCpiIeEsBExFvKWAi4i0FTES8ZZxzG70NIiK3RSMwEfGWAiYi3lLARMRbCpiIeEsBExFvKWAi4q3/D6HuRuV7+koeAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch : 30\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch : 40\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch : 50\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch : 100\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch : 200\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch : 300\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch : 400\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch : 500\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "epochs = ['1', '10', '20', '30', '40', '50', '100', '200', '300', '400', '500']\n",
    "for e in epochs:\n",
    "    print(f'epoch : {e}')\n",
    "    model.load_weights(f'ckpt/keyboard_{e}.ckpt')\n",
    "\n",
    "    test_input = tf.convert_to_tensor(test_data, dtype=tf.float64)\n",
    "    pred = model.predict_classes(test_input)\n",
    "    pred = tf.reshape(pred, [1280, 960]).numpy()\n",
    "\n",
    "    test = np.full((960, 1280, 3), 255, np.uint8)\n",
    "    for y in range(960):\n",
    "        for x in range(1280):\n",
    "            test[y][x] = colors[pred[x][y]]#pred[x][y]\n",
    "    \n",
    "    plt.axis('off')\n",
    "    plt.imshow(test)\n",
    "    plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 8. 모델이 생성한 결과로 가상 인터페이스 버튼 후보정"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data = pd.read_hdf('1280x960.h5', 'df')\n",
    "test_input = tf.convert_to_tensor(test_data, dtype=tf.float64)\n",
    "pred = model.predict_classes(test_input)\n",
    "pred = tf.reshape(pred, [1280, 960]).numpy()\n",
    "\n",
    "test = np.full((960, 1280, 3), 255, np.uint8)\n",
    "for y in range(960):\n",
    "    for x in range(1280):\n",
    "        test[y][x] = pred[x][y]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "min y : 642\n",
      "min x : 984\n",
      "max y : 723\n",
      "max x : 1212\n",
      "min y : 615\n",
      "min x : 596\n",
      "max y : 709\n",
      "max x : 785\n",
      "min y : 619\n",
      "min x : 174\n",
      "max y : 701\n",
      "max x : 396\n",
      "min y : 439\n",
      "min x : 833\n",
      "max y : 521\n",
      "max x : 1118\n",
      "min y : 505\n",
      "min x : 602\n",
      "max y : 591\n",
      "max x : 765\n",
      "min y : 419\n",
      "min x : 276\n",
      "max y : 498\n",
      "max x : 521\n"
     ]
    }
   ],
   "source": [
    "# 0, 1 클래스를 뺀 나머지 인덱스 수 만큼 반복\n",
    "# 0, 1 클래스는 배경과 라인이기 때문에 제외함\n",
    "for index in range(1, model.output.shape[1]):\n",
    "    print(f'min y : {min(np.where(test==index)[0])}')\n",
    "    print(f'min x : {min(np.where(test==index)[1])}')\n",
    "    print(f'max y : {max(np.where(test==index)[0])}')\n",
    "    print(f'max x : {max(np.where(test==index)[1])}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2a3a18b6288>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 0 클래스를 뺀 나머지 인덱스 수 만큼 반복\n",
    "# 0 클래스는 배경과 라인이기 때문에 제외함\n",
    "img = np.full((960, 1280, 3), 255, np.uint8)\n",
    "\n",
    "for index in range(1, model.output.shape[1]):\n",
    "    min_y = min(np.where(test==index)[0])\n",
    "    min_x = min(np.where(test==index)[1])\n",
    "    max_y = max(np.where(test==index)[0])\n",
    "    max_x = max(np.where(test==index)[1])\n",
    "\n",
    "    cv2.rectangle(img, (min_x,min_y), (max_x,max_y), (0, 255, 0), thickness=2)\n",
    "\n",
    "plt.imshow(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = load_model('demo_keyboard_model')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data2 = pd.DataFrame(columns=['x','y'])\n",
    "for x in range(200):\n",
    "    for y in range(200):\n",
    "        temp = pd.DataFrame({'x':[x], 'y':[y]})\n",
    "\n",
    "        test_data2 = test_data2.append(temp, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\KYH\\.conda\\envs\\tensor\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\sequential.py:455: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype(\"int32\")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).\n",
      "  warnings.warn('`model.predict_classes()` is deprecated and '\n"
     ]
    }
   ],
   "source": [
    "test_data2['x'] = test_data2['x']/200\n",
    "test_data2['y'] = test_data2['y']/200\n",
    "test_input2 = tf.convert_to_tensor(test_data2, dtype=tf.float64)\n",
    "pred2 = model.predict_classes(test_input2)\n",
    "pred2 = tf.reshape(pred2, [200, 200]).numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "test2 = np.full((200, 200, 3), 255, np.uint8)\n",
    "for y in range(200):\n",
    "    print(y)\n",
    "    for x in range(200):\n",
    "        test2[y][x] = colors[pred2[x][y]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2a3a1b05d88>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plt.axis('off')\n",
    "plt.imshow(test2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data3 = pd.DataFrame(columns=['x','y'])\n",
    "for x in range(800):\n",
    "    for y in range(600):\n",
    "        temp = pd.DataFrame({'x':[x], 'y':[y]})\n",
    "\n",
    "        test_data3 = test_data3.append(temp, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data3['x'] = test_data3['x']/800\n",
    "test_data3['y'] = test_data3['y']/600\n",
    "test_input3 = tf.convert_to_tensor(test_data3, dtype=tf.float64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred3 = model.predict_classes(test_input3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred3 = tf.reshape(pred3, [800, 600]).numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "test3 = np.full((600, 800, 3), 255, np.uint8)\n",
    "for y in range(600):\n",
    "    print(y)\n",
    "    for x in range(800):\n",
    "        test3[y][x] = colors[pred3[x][y]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2a3a0992ec8>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plt.axis('off')\n",
    "plt.imshow(test3)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.7.11 ('tensor')",
   "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": "3.7.11"
  },
  "vscode": {
   "interpreter": {
    "hash": "4a399d4c4514d125852f3bc51ca0a6706781da022d47c4cd91c80cf905a19a6d"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}