8. ColabSpecial.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0072b067",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: tensorflow in e:\\github\\ml practiacal\\.venv\\lib\\site-packages (2.19.0)\n",
      "Requirement already satisfied: absl-py>=1.0.0 in e:\\github\\ml practiacal\\.venv\\lib\\site-packages (from tensorflow) (2.2.2)\n",
      "Requirement already satisfied: astunparse>=1.6.0 in e:\\github\\ml practiacal\\.venv\\lib\\site-packages (from tensorflow) (1.6.3)\n",
      "Requirement already satisfied: flatbuffers>=24.3.25 in e:\\github\\ml practiacal\\.venv\\lib\\site-packages (from tensorflow) (25.2.10)\n",
      "Requirement already satisfied: gast!=0.5.0,!=0.5.1,!=0.5.2,>=0.2.1 in e:\\github\\ml practiacal\\.venv\\lib\\site-packages (from tensorflow) (0.6.0)\n",
      "Requirement already satisfied: google-pasta>=0.1.1 in e:\\github\\ml practiacal\\.venv\\lib\\site-packages (from tensorflow) (0.2.0)\n",
      "Requirement already satisfied: libclang>=13.0.0 in e:\\github\\ml practiacal\\.venv\\lib\\site-packages (from tensorflow) (18.1.1)\n",
      "Requirement already satisfied: opt-einsum>=2.3.2 in e:\\github\\ml practiacal\\.venv\\lib\\site-packages (from tensorflow) (3.4.0)\n",
      "Requirement already satisfied: packaging in e:\\github\\ml practiacal\\.venv\\lib\\site-packages (from tensorflow) (24.2)\n",
      "Requirement already satisfied: protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<6.0.0dev,>=3.20.3 in e:\\github\\ml practiacal\\.venv\\lib\\site-packages (from tensorflow) (5.29.4)\n",
      "Requirement already satisfied: requests<3,>=2.21.0 in e:\\github\\ml practiacal\\.venv\\lib\\site-packages (from tensorflow) (2.32.3)\n",
      "Requirement already satisfied: setuptools in e:\\github\\ml practiacal\\.venv\\lib\\site-packages (from tensorflow) (78.1.0)\n",
      "Requirement already satisfied: six>=1.12.0 in e:\\github\\ml practiacal\\.venv\\lib\\site-packages (from tensorflow) (1.17.0)\n",
      "Requirement already satisfied: termcolor>=1.1.0 in e:\\github\\ml practiacal\\.venv\\lib\\site-packages (from tensorflow) (3.0.1)\n",
      "Requirement already satisfied: typing-extensions>=3.6.6 in e:\\github\\ml practiacal\\.venv\\lib\\site-packages (from tensorflow) (4.13.1)\n",
      "Requirement already satisfied: wrapt>=1.11.0 in e:\\github\\ml practiacal\\.venv\\lib\\site-packages (from tensorflow) (1.17.2)\n",
      "Requirement already satisfied: grpcio<2.0,>=1.24.3 in e:\\github\\ml practiacal\\.venv\\lib\\site-packages (from tensorflow) (1.71.0)\n",
      "Requirement already satisfied: tensorboard~=2.19.0 in e:\\github\\ml practiacal\\.venv\\lib\\site-packages (from tensorflow) (2.19.0)\n",
      "Requirement already satisfied: keras>=3.5.0 in e:\\github\\ml practiacal\\.venv\\lib\\site-packages (from tensorflow) (3.9.2)\n",
      "Collecting numpy<2.2.0,>=1.26.0 (from tensorflow)\n",
      "  Using cached numpy-2.1.3-cp312-cp312-win_amd64.whl.metadata (60 kB)\n",
      "Requirement already satisfied: h5py>=3.11.0 in e:\\github\\ml practiacal\\.venv\\lib\\site-packages (from tensorflow) (3.13.0)\n",
      "Requirement already satisfied: ml-dtypes<1.0.0,>=0.5.1 in e:\\github\\ml practiacal\\.venv\\lib\\site-packages (from tensorflow) (0.5.1)\n",
      "Requirement already satisfied: wheel<1.0,>=0.23.0 in e:\\github\\ml practiacal\\.venv\\lib\\site-packages (from astunparse>=1.6.0->tensorflow) (0.45.1)\n",
      "Requirement already satisfied: rich in e:\\github\\ml practiacal\\.venv\\lib\\site-packages (from keras>=3.5.0->tensorflow) (14.0.0)\n",
      "Requirement already satisfied: namex in e:\\github\\ml practiacal\\.venv\\lib\\site-packages (from keras>=3.5.0->tensorflow) (0.0.8)\n",
      "Requirement already satisfied: optree in e:\\github\\ml practiacal\\.venv\\lib\\site-packages (from keras>=3.5.0->tensorflow) (0.14.1)\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in e:\\github\\ml practiacal\\.venv\\lib\\site-packages (from requests<3,>=2.21.0->tensorflow) (3.4.1)\n",
      "Requirement already satisfied: idna<4,>=2.5 in e:\\github\\ml practiacal\\.venv\\lib\\site-packages (from requests<3,>=2.21.0->tensorflow) (3.10)\n",
      "Requirement already satisfied: urllib3<3,>=1.21.1 in e:\\github\\ml practiacal\\.venv\\lib\\site-packages (from requests<3,>=2.21.0->tensorflow) (2.3.0)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in e:\\github\\ml practiacal\\.venv\\lib\\site-packages (from requests<3,>=2.21.0->tensorflow) (2025.1.31)\n",
      "Requirement already satisfied: markdown>=2.6.8 in e:\\github\\ml practiacal\\.venv\\lib\\site-packages (from tensorboard~=2.19.0->tensorflow) (3.7)\n",
      "Requirement already satisfied: tensorboard-data-server<0.8.0,>=0.7.0 in e:\\github\\ml practiacal\\.venv\\lib\\site-packages (from tensorboard~=2.19.0->tensorflow) (0.7.2)\n",
      "Requirement already satisfied: werkzeug>=1.0.1 in e:\\github\\ml practiacal\\.venv\\lib\\site-packages (from tensorboard~=2.19.0->tensorflow) (3.1.3)\n",
      "Requirement already satisfied: MarkupSafe>=2.1.1 in e:\\github\\ml practiacal\\.venv\\lib\\site-packages (from werkzeug>=1.0.1->tensorboard~=2.19.0->tensorflow) (3.0.2)\n",
      "Requirement already satisfied: markdown-it-py>=2.2.0 in e:\\github\\ml practiacal\\.venv\\lib\\site-packages (from rich->keras>=3.5.0->tensorflow) (3.0.0)\n",
      "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in e:\\github\\ml practiacal\\.venv\\lib\\site-packages (from rich->keras>=3.5.0->tensorflow) (2.19.1)\n",
      "Requirement already satisfied: mdurl~=0.1 in e:\\github\\ml practiacal\\.venv\\lib\\site-packages (from markdown-it-py>=2.2.0->rich->keras>=3.5.0->tensorflow) (0.1.2)\n",
      "Using cached numpy-2.1.3-cp312-cp312-win_amd64.whl (12.6 MB)\n",
      "Installing collected packages: numpy\n",
      "  Attempting uninstall: numpy\n",
      "    Found existing installation: numpy 2.2.4\n",
      "    Uninstalling numpy-2.2.4:\n",
      "      Successfully uninstalled numpy-2.2.4\n",
      "Successfully installed numpy-2.1.3\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  WARNING: Failed to remove contents in a temporary directory 'E:\\GitHub\\ML Practiacal\\.venv\\Lib\\site-packages\\~-mpy.libs'.\n",
      "  You can safely remove it manually.\n",
      "  WARNING: Failed to remove contents in a temporary directory 'E:\\GitHub\\ML Practiacal\\.venv\\Lib\\site-packages\\~-mpy'.\n",
      "  You can safely remove it manually.\n"
     ]
    }
   ],
   "source": [
    "#install tensorflow\n",
    "!pip install tensorflow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "19242a3f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: numpy in e:\\github\\ml practiacal\\.venv\\lib\\site-packages (2.1.3)\n",
      "Collecting numpy\n",
      "  Using cached numpy-2.2.4-cp312-cp312-win_amd64.whl.metadata (60 kB)\n",
      "Using cached numpy-2.2.4-cp312-cp312-win_amd64.whl (12.6 MB)\n",
      "Installing collected packages: numpy\n",
      "  Attempting uninstall: numpy\n",
      "    Found existing installation: numpy 2.1.3\n",
      "    Uninstalling numpy-2.1.3:\n",
      "      Successfully uninstalled numpy-2.1.3\n",
      "Successfully installed numpy-2.2.4\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
      "tensorflow 2.19.0 requires numpy<2.2.0,>=1.26.0, but you have numpy 2.2.4 which is incompatible.\n"
     ]
    }
   ],
   "source": [
    "# Ensure compatibility between TensorFlow and NumPy\n",
    "%pip install --upgrade numpy\n",
    "\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7b4a63b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "tf.random.set_seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "bccc60c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.array([\n",
    "    [0,0],\n",
    "    [0,1],\n",
    "    [1,0],\n",
    "    [1,1]\n",
    "],dtype=float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ee365813",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = np.array([\n",
    "    [0],\n",
    "    [1],\n",
    "    [1],\n",
    "    [0]\n",
    "    ],dtype=float)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1dd53b99",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "e:\\GitHub\\ML Practiacal\\.venv\\Lib\\site-packages\\keras\\src\\layers\\core\\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "#Define the model\n",
    "model = tf.keras.models.Sequential([\n",
    "    tf.keras.layers.Dense(2, activation='relu', input_shape=(2,)),\n",
    "    tf.keras.layers.Dense(1, activation='sigmoid')\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "165c7050",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(loss=\"mean_squared_error\",\n",
    "            optimizer=tf.keras.optimizers.SGD(learning_rate=0.1),\n",
    "            metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "eaf9c58a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 474ms/step - accuracy: 0.7500 - loss: 0.3007\n",
      "Epoch 2/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - accuracy: 0.7500 - loss: 0.3003\n",
      "Epoch 3/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - accuracy: 0.7500 - loss: 0.2999\n",
      "Epoch 4/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - accuracy: 0.7500 - loss: 0.2995\n",
      "Epoch 5/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - accuracy: 0.7500 - loss: 0.2990\n",
      "Epoch 6/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - accuracy: 0.7500 - loss: 0.2986\n",
      "Epoch 7/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - accuracy: 0.7500 - loss: 0.2981\n",
      "Epoch 8/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - accuracy: 0.7500 - loss: 0.2977\n",
      "Epoch 9/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - accuracy: 0.7500 - loss: 0.2973\n",
      "Epoch 10/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - accuracy: 0.7500 - loss: 0.2968\n",
      "Epoch 11/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - accuracy: 0.7500 - loss: 0.2965\n",
      "Epoch 12/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - accuracy: 0.7500 - loss: 0.2960\n",
      "Epoch 13/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - accuracy: 0.7500 - loss: 0.2956\n",
      "Epoch 14/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - accuracy: 0.7500 - loss: 0.2952\n",
      "Epoch 15/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - accuracy: 0.7500 - loss: 0.2948\n",
      "Epoch 16/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - accuracy: 0.7500 - loss: 0.2943\n",
      "Epoch 17/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 99ms/step - accuracy: 0.7500 - loss: 0.2939\n",
      "Epoch 18/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 101ms/step - accuracy: 0.7500 - loss: 0.2935\n",
      "Epoch 19/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 87ms/step - accuracy: 0.7500 - loss: 0.2931\n",
      "Epoch 20/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - accuracy: 0.7500 - loss: 0.2927\n",
      "Epoch 21/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 207ms/step - accuracy: 0.7500 - loss: 0.2923\n",
      "Epoch 22/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 108ms/step - accuracy: 0.7500 - loss: 0.2919\n",
      "Epoch 23/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 88ms/step - accuracy: 0.7500 - loss: 0.2915\n",
      "Epoch 24/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - accuracy: 0.7500 - loss: 0.2911\n",
      "Epoch 25/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85ms/step - accuracy: 0.7500 - loss: 0.2907\n",
      "Epoch 26/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - accuracy: 0.7500 - loss: 0.2903\n",
      "Epoch 27/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 86ms/step - accuracy: 0.7500 - loss: 0.2899\n",
      "Epoch 28/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - accuracy: 0.7500 - loss: 0.2895\n",
      "Epoch 29/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - accuracy: 0.7500 - loss: 0.2891\n",
      "Epoch 30/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - accuracy: 0.7500 - loss: 0.2888\n",
      "Epoch 31/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - accuracy: 0.7500 - loss: 0.2883\n",
      "Epoch 32/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - accuracy: 0.7500 - loss: 0.2879\n",
      "Epoch 33/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - accuracy: 0.7500 - loss: 0.2875\n",
      "Epoch 34/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - accuracy: 0.7500 - loss: 0.2871\n",
      "Epoch 35/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - accuracy: 0.7500 - loss: 0.2868\n",
      "Epoch 36/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - accuracy: 0.7500 - loss: 0.2864\n",
      "Epoch 37/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - accuracy: 0.7500 - loss: 0.2860\n",
      "Epoch 38/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - accuracy: 0.7500 - loss: 0.2856\n",
      "Epoch 39/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - accuracy: 0.7500 - loss: 0.2853\n",
      "Epoch 40/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - accuracy: 0.7500 - loss: 0.2849\n",
      "Epoch 41/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - accuracy: 0.7500 - loss: 0.2845\n",
      "Epoch 42/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - accuracy: 0.7500 - loss: 0.2841\n",
      "Epoch 43/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - accuracy: 0.7500 - loss: 0.2838\n",
      "Epoch 44/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - accuracy: 0.7500 - loss: 0.2834\n",
      "Epoch 45/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - accuracy: 0.7500 - loss: 0.2830\n",
      "Epoch 46/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - accuracy: 0.7500 - loss: 0.2826\n",
      "Epoch 47/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - accuracy: 0.7500 - loss: 0.2823\n",
      "Epoch 48/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - accuracy: 0.7500 - loss: 0.2820\n",
      "Epoch 49/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - accuracy: 0.7500 - loss: 0.2816\n",
      "Epoch 50/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - accuracy: 0.7500 - loss: 0.2812\n",
      "Epoch 51/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - accuracy: 0.7500 - loss: 0.2809\n",
      "Epoch 52/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - accuracy: 0.7500 - loss: 0.2805\n",
      "Epoch 53/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - accuracy: 0.7500 - loss: 0.2801\n",
      "Epoch 54/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - accuracy: 0.7500 - loss: 0.2798\n",
      "Epoch 55/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - accuracy: 0.7500 - loss: 0.2795\n",
      "Epoch 56/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - accuracy: 0.7500 - loss: 0.2791\n",
      "Epoch 57/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - accuracy: 0.7500 - loss: 0.2787\n",
      "Epoch 58/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - accuracy: 0.7500 - loss: 0.2784\n",
      "Epoch 59/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - accuracy: 0.7500 - loss: 0.2781\n",
      "Epoch 60/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - accuracy: 0.7500 - loss: 0.2777\n",
      "Epoch 61/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - accuracy: 0.7500 - loss: 0.2774\n",
      "Epoch 62/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - accuracy: 0.7500 - loss: 0.2770\n",
      "Epoch 63/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - accuracy: 0.7500 - loss: 0.2767\n",
      "Epoch 64/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - accuracy: 0.7500 - loss: 0.2764\n",
      "Epoch 65/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - accuracy: 0.7500 - loss: 0.2760\n",
      "Epoch 66/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - accuracy: 0.7500 - loss: 0.2757\n",
      "Epoch 67/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - accuracy: 0.7500 - loss: 0.2754\n",
      "Epoch 68/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - accuracy: 0.7500 - loss: 0.2751\n",
      "Epoch 69/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - accuracy: 0.7500 - loss: 0.2748\n",
      "Epoch 70/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - accuracy: 0.7500 - loss: 0.2744\n",
      "Epoch 71/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - accuracy: 0.7500 - loss: 0.2741\n",
      "Epoch 72/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - accuracy: 0.7500 - loss: 0.2738\n",
      "Epoch 73/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - accuracy: 0.7500 - loss: 0.2735\n",
      "Epoch 74/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - accuracy: 0.7500 - loss: 0.2732\n",
      "Epoch 75/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - accuracy: 0.7500 - loss: 0.2729\n",
      "Epoch 76/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - accuracy: 0.7500 - loss: 0.2726\n",
      "Epoch 77/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - accuracy: 0.7500 - loss: 0.2723\n",
      "Epoch 78/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - accuracy: 0.7500 - loss: 0.2720\n",
      "Epoch 79/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - accuracy: 0.7500 - loss: 0.2717\n",
      "Epoch 80/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - accuracy: 0.7500 - loss: 0.2714\n",
      "Epoch 81/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - accuracy: 0.7500 - loss: 0.2712\n",
      "Epoch 82/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - accuracy: 0.7500 - loss: 0.2708\n",
      "Epoch 83/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - accuracy: 0.7500 - loss: 0.2706\n",
      "Epoch 84/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - accuracy: 0.7500 - loss: 0.2703\n",
      "Epoch 85/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - accuracy: 0.7500 - loss: 0.2701\n",
      "Epoch 86/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - accuracy: 0.7500 - loss: 0.2698\n",
      "Epoch 87/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - accuracy: 0.7500 - loss: 0.2695\n",
      "Epoch 88/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - accuracy: 0.7500 - loss: 0.2692\n",
      "Epoch 89/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - accuracy: 0.7500 - loss: 0.2690\n",
      "Epoch 90/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - accuracy: 0.7500 - loss: 0.2687\n",
      "Epoch 91/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - accuracy: 0.7500 - loss: 0.2685\n",
      "Epoch 92/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - accuracy: 0.7500 - loss: 0.2682\n",
      "Epoch 93/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - accuracy: 0.7500 - loss: 0.2680\n",
      "Epoch 94/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - accuracy: 0.7500 - loss: 0.2677\n",
      "Epoch 95/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - accuracy: 0.7500 - loss: 0.2675\n",
      "Epoch 96/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - accuracy: 0.7500 - loss: 0.2673\n",
      "Epoch 97/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - accuracy: 0.7500 - loss: 0.2670\n",
      "Epoch 98/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - accuracy: 0.7500 - loss: 0.2668\n",
      "Epoch 99/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - accuracy: 0.7500 - loss: 0.2666\n",
      "Epoch 100/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - accuracy: 0.7500 - loss: 0.2664\n",
      "Epoch 101/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - accuracy: 0.7500 - loss: 0.2661\n",
      "Epoch 102/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - accuracy: 0.7500 - loss: 0.2659\n",
      "Epoch 103/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - accuracy: 0.7500 - loss: 0.2657\n",
      "Epoch 104/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - accuracy: 0.7500 - loss: 0.2655\n",
      "Epoch 105/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - accuracy: 0.7500 - loss: 0.2653\n",
      "Epoch 106/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - accuracy: 0.7500 - loss: 0.2651\n",
      "Epoch 107/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - accuracy: 0.7500 - loss: 0.2649\n",
      "Epoch 108/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - accuracy: 0.7500 - loss: 0.2647\n",
      "Epoch 109/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - accuracy: 0.7500 - loss: 0.2645\n",
      "Epoch 110/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - accuracy: 0.7500 - loss: 0.2643\n",
      "Epoch 111/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - accuracy: 0.7500 - loss: 0.2642\n",
      "Epoch 112/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - accuracy: 0.7500 - loss: 0.2640\n",
      "Epoch 113/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - accuracy: 0.7500 - loss: 0.2638\n",
      "Epoch 114/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - accuracy: 0.7500 - loss: 0.2636\n",
      "Epoch 115/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - accuracy: 0.7500 - loss: 0.2635\n",
      "Epoch 116/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - accuracy: 0.7500 - loss: 0.2633\n",
      "Epoch 117/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - accuracy: 0.7500 - loss: 0.2631\n",
      "Epoch 118/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - accuracy: 0.7500 - loss: 0.2630\n",
      "Epoch 119/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - accuracy: 0.7500 - loss: 0.2628\n",
      "Epoch 120/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - accuracy: 0.7500 - loss: 0.2627\n",
      "Epoch 121/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - accuracy: 0.7500 - loss: 0.2625\n",
      "Epoch 122/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - accuracy: 0.7500 - loss: 0.2623\n",
      "Epoch 123/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 110ms/step - accuracy: 0.7500 - loss: 0.2622\n",
      "Epoch 124/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - accuracy: 0.7500 - loss: 0.2621\n",
      "Epoch 125/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - accuracy: 0.7500 - loss: 0.2619\n",
      "Epoch 126/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - accuracy: 0.7500 - loss: 0.2618\n",
      "Epoch 127/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - accuracy: 0.7500 - loss: 0.2616\n",
      "Epoch 128/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - accuracy: 0.7500 - loss: 0.2615\n",
      "Epoch 129/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - accuracy: 0.7500 - loss: 0.2614\n",
      "Epoch 130/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - accuracy: 0.7500 - loss: 0.2613\n",
      "Epoch 131/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - accuracy: 0.7500 - loss: 0.2611\n",
      "Epoch 132/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - accuracy: 0.7500 - loss: 0.2610\n",
      "Epoch 133/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - accuracy: 0.7500 - loss: 0.2609\n",
      "Epoch 134/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - accuracy: 0.7500 - loss: 0.2608\n",
      "Epoch 135/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - accuracy: 0.7500 - loss: 0.2607\n",
      "Epoch 136/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - accuracy: 0.7500 - loss: 0.2605\n",
      "Epoch 137/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - accuracy: 0.7500 - loss: 0.2604\n",
      "Epoch 138/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - accuracy: 0.7500 - loss: 0.2603\n",
      "Epoch 139/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - accuracy: 0.7500 - loss: 0.2602\n",
      "Epoch 140/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - accuracy: 0.7500 - loss: 0.2601\n",
      "Epoch 141/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - accuracy: 0.7500 - loss: 0.2600\n",
      "Epoch 142/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - accuracy: 0.7500 - loss: 0.2599\n",
      "Epoch 143/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - accuracy: 0.7500 - loss: 0.2598\n",
      "Epoch 144/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - accuracy: 0.7500 - loss: 0.2597\n",
      "Epoch 145/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - accuracy: 0.7500 - loss: 0.2596\n",
      "Epoch 146/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - accuracy: 0.7500 - loss: 0.2595\n",
      "Epoch 147/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - accuracy: 0.7500 - loss: 0.2594\n",
      "Epoch 148/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - accuracy: 0.7500 - loss: 0.2593\n",
      "Epoch 149/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - accuracy: 0.7500 - loss: 0.2593\n",
      "Epoch 150/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - accuracy: 0.7500 - loss: 0.2592\n",
      "Epoch 151/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - accuracy: 0.7500 - loss: 0.2591\n",
      "Epoch 152/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - accuracy: 0.7500 - loss: 0.2590\n",
      "Epoch 153/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - accuracy: 0.7500 - loss: 0.2589\n",
      "Epoch 154/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - accuracy: 0.7500 - loss: 0.2588\n",
      "Epoch 155/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - accuracy: 0.7500 - loss: 0.2588\n",
      "Epoch 156/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - accuracy: 0.7500 - loss: 0.2587\n",
      "Epoch 157/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - accuracy: 0.7500 - loss: 0.2586\n",
      "Epoch 158/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - accuracy: 0.7500 - loss: 0.2585\n",
      "Epoch 159/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - accuracy: 0.7500 - loss: 0.2585\n",
      "Epoch 160/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - accuracy: 0.7500 - loss: 0.2584\n",
      "Epoch 161/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - accuracy: 0.7500 - loss: 0.2583\n",
      "Epoch 162/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - accuracy: 0.7500 - loss: 0.2583\n",
      "Epoch 163/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - accuracy: 0.7500 - loss: 0.2582\n",
      "Epoch 164/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - accuracy: 0.7500 - loss: 0.2581\n",
      "Epoch 165/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - accuracy: 0.7500 - loss: 0.2581\n",
      "Epoch 166/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - accuracy: 0.7500 - loss: 0.2580\n",
      "Epoch 167/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - accuracy: 0.7500 - loss: 0.2579\n",
      "Epoch 168/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - accuracy: 0.7500 - loss: 0.2579\n",
      "Epoch 169/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - accuracy: 0.7500 - loss: 0.2578\n",
      "Epoch 170/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - accuracy: 0.7500 - loss: 0.2578\n",
      "Epoch 171/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - accuracy: 0.7500 - loss: 0.2577\n",
      "Epoch 172/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - accuracy: 0.7500 - loss: 0.2576\n",
      "Epoch 173/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - accuracy: 0.7500 - loss: 0.2576\n",
      "Epoch 174/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - accuracy: 0.7500 - loss: 0.2575\n",
      "Epoch 175/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - accuracy: 0.7500 - loss: 0.2575\n",
      "Epoch 176/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - accuracy: 0.7500 - loss: 0.2574\n",
      "Epoch 177/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 97ms/step - accuracy: 0.7500 - loss: 0.2574\n",
      "Epoch 178/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - accuracy: 0.7500 - loss: 0.2573\n",
      "Epoch 179/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - accuracy: 0.7500 - loss: 0.2573\n",
      "Epoch 180/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - accuracy: 0.7500 - loss: 0.2572\n",
      "Epoch 181/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - accuracy: 0.7500 - loss: 0.2572\n",
      "Epoch 182/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - accuracy: 0.7500 - loss: 0.2571\n",
      "Epoch 183/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - accuracy: 0.7500 - loss: 0.2571\n",
      "Epoch 184/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - accuracy: 0.7500 - loss: 0.2570\n",
      "Epoch 185/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - accuracy: 0.7500 - loss: 0.2570\n",
      "Epoch 186/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - accuracy: 0.7500 - loss: 0.2569\n",
      "Epoch 187/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - accuracy: 0.7500 - loss: 0.2569\n",
      "Epoch 188/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - accuracy: 0.7500 - loss: 0.2569\n",
      "Epoch 189/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - accuracy: 0.7500 - loss: 0.2568\n",
      "Epoch 190/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - accuracy: 0.7500 - loss: 0.2568\n",
      "Epoch 191/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - accuracy: 0.7500 - loss: 0.2567\n",
      "Epoch 192/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - accuracy: 0.7500 - loss: 0.2567\n",
      "Epoch 193/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - accuracy: 0.7500 - loss: 0.2566\n",
      "Epoch 194/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - accuracy: 0.7500 - loss: 0.2566\n",
      "Epoch 195/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - accuracy: 0.7500 - loss: 0.2566\n",
      "Epoch 196/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - accuracy: 0.7500 - loss: 0.2565\n",
      "Epoch 197/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - accuracy: 0.7500 - loss: 0.2565\n",
      "Epoch 198/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - accuracy: 0.7500 - loss: 0.2564\n",
      "Epoch 199/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - accuracy: 0.7500 - loss: 0.2564\n",
      "Epoch 200/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - accuracy: 0.7500 - loss: 0.2564\n",
      "Epoch 201/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - accuracy: 0.7500 - loss: 0.2563\n",
      "Epoch 202/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - accuracy: 0.7500 - loss: 0.2563\n",
      "Epoch 203/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - accuracy: 0.7500 - loss: 0.2563\n",
      "Epoch 204/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - accuracy: 0.7500 - loss: 0.2562\n",
      "Epoch 205/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - accuracy: 0.7500 - loss: 0.2562\n",
      "Epoch 206/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - accuracy: 0.7500 - loss: 0.2561\n",
      "Epoch 207/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - accuracy: 0.7500 - loss: 0.2561\n",
      "Epoch 208/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - accuracy: 0.5000 - loss: 0.2561\n",
      "Epoch 209/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - accuracy: 0.5000 - loss: 0.2560\n",
      "Epoch 210/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - accuracy: 0.5000 - loss: 0.2560\n",
      "Epoch 211/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - accuracy: 0.5000 - loss: 0.2560\n",
      "Epoch 212/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 101ms/step - accuracy: 0.5000 - loss: 0.2559\n",
      "Epoch 213/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - accuracy: 0.5000 - loss: 0.2559\n",
      "Epoch 214/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - accuracy: 0.5000 - loss: 0.2559\n",
      "Epoch 215/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - accuracy: 0.5000 - loss: 0.2558\n",
      "Epoch 216/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 87ms/step - accuracy: 0.5000 - loss: 0.2558\n",
      "Epoch 217/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - accuracy: 0.5000 - loss: 0.2558\n",
      "Epoch 218/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - accuracy: 0.5000 - loss: 0.2557\n",
      "Epoch 219/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - accuracy: 0.5000 - loss: 0.2557\n",
      "Epoch 220/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - accuracy: 0.5000 - loss: 0.2557\n",
      "Epoch 221/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - accuracy: 0.5000 - loss: 0.2557\n",
      "Epoch 222/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - accuracy: 0.5000 - loss: 0.2556\n",
      "Epoch 223/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - accuracy: 0.5000 - loss: 0.2556\n",
      "Epoch 224/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - accuracy: 0.5000 - loss: 0.2556\n",
      "Epoch 225/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - accuracy: 0.5000 - loss: 0.2555\n",
      "Epoch 226/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - accuracy: 0.5000 - loss: 0.2555\n",
      "Epoch 227/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - accuracy: 0.5000 - loss: 0.2555\n",
      "Epoch 228/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - accuracy: 0.5000 - loss: 0.2555\n",
      "Epoch 229/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - accuracy: 0.5000 - loss: 0.2554\n",
      "Epoch 230/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - accuracy: 0.5000 - loss: 0.2554\n",
      "Epoch 231/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - accuracy: 0.5000 - loss: 0.2554\n",
      "Epoch 232/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - accuracy: 0.5000 - loss: 0.2553\n",
      "Epoch 233/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - accuracy: 0.5000 - loss: 0.2553\n",
      "Epoch 234/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - accuracy: 0.5000 - loss: 0.2553\n",
      "Epoch 235/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - accuracy: 0.5000 - loss: 0.2553\n",
      "Epoch 236/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - accuracy: 0.5000 - loss: 0.2552\n",
      "Epoch 237/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - accuracy: 0.5000 - loss: 0.2552\n",
      "Epoch 238/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - accuracy: 0.5000 - loss: 0.2552\n",
      "Epoch 239/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - accuracy: 0.5000 - loss: 0.2552\n",
      "Epoch 240/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - accuracy: 0.5000 - loss: 0.2551\n",
      "Epoch 241/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - accuracy: 0.5000 - loss: 0.2551\n",
      "Epoch 242/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - accuracy: 0.5000 - loss: 0.2551\n",
      "Epoch 243/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - accuracy: 0.5000 - loss: 0.2550\n",
      "Epoch 244/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - accuracy: 0.5000 - loss: 0.2550\n",
      "Epoch 245/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - accuracy: 0.5000 - loss: 0.2550\n",
      "Epoch 246/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - accuracy: 0.5000 - loss: 0.2550\n",
      "Epoch 247/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - accuracy: 0.5000 - loss: 0.2549\n",
      "Epoch 248/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - accuracy: 0.5000 - loss: 0.2549\n",
      "Epoch 249/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - accuracy: 0.5000 - loss: 0.2549\n",
      "Epoch 250/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - accuracy: 0.5000 - loss: 0.2549\n",
      "Epoch 251/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - accuracy: 0.5000 - loss: 0.2549\n",
      "Epoch 252/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - accuracy: 0.5000 - loss: 0.2548\n",
      "Epoch 253/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - accuracy: 0.5000 - loss: 0.2548\n",
      "Epoch 254/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - accuracy: 0.5000 - loss: 0.2548\n",
      "Epoch 255/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - accuracy: 0.5000 - loss: 0.2548\n",
      "Epoch 256/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - accuracy: 0.5000 - loss: 0.2547\n",
      "Epoch 257/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - accuracy: 0.5000 - loss: 0.2547\n",
      "Epoch 258/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - accuracy: 0.5000 - loss: 0.2547\n",
      "Epoch 259/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - accuracy: 0.5000 - loss: 0.2547\n",
      "Epoch 260/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - accuracy: 0.5000 - loss: 0.2546\n",
      "Epoch 261/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - accuracy: 0.5000 - loss: 0.2546\n",
      "Epoch 262/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - accuracy: 0.5000 - loss: 0.2546\n",
      "Epoch 263/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 112ms/step - accuracy: 0.5000 - loss: 0.2546\n",
      "Epoch 264/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - accuracy: 0.5000 - loss: 0.2546\n",
      "Epoch 265/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - accuracy: 0.5000 - loss: 0.2545\n",
      "Epoch 266/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - accuracy: 0.5000 - loss: 0.2545\n",
      "Epoch 267/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - accuracy: 0.5000 - loss: 0.2545\n",
      "Epoch 268/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - accuracy: 0.5000 - loss: 0.2545\n",
      "Epoch 269/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - accuracy: 0.5000 - loss: 0.2544\n",
      "Epoch 270/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - accuracy: 0.5000 - loss: 0.2544\n",
      "Epoch 271/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - accuracy: 0.5000 - loss: 0.2544\n",
      "Epoch 272/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - accuracy: 0.5000 - loss: 0.2544\n",
      "Epoch 273/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - accuracy: 0.5000 - loss: 0.2544\n",
      "Epoch 274/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - accuracy: 0.5000 - loss: 0.2543\n",
      "Epoch 275/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - accuracy: 0.5000 - loss: 0.2543\n",
      "Epoch 276/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - accuracy: 0.5000 - loss: 0.2543\n",
      "Epoch 277/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - accuracy: 0.5000 - loss: 0.2543\n",
      "Epoch 278/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - accuracy: 0.5000 - loss: 0.2543\n",
      "Epoch 279/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - accuracy: 0.5000 - loss: 0.2542\n",
      "Epoch 280/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - accuracy: 0.5000 - loss: 0.2542\n",
      "Epoch 281/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - accuracy: 0.5000 - loss: 0.2542\n",
      "Epoch 282/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - accuracy: 0.5000 - loss: 0.2542\n",
      "Epoch 283/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - accuracy: 0.5000 - loss: 0.2542\n",
      "Epoch 284/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 185ms/step - accuracy: 0.5000 - loss: 0.2541\n",
      "Epoch 285/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - accuracy: 0.5000 - loss: 0.2541\n",
      "Epoch 286/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - accuracy: 0.5000 - loss: 0.2541\n",
      "Epoch 287/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - accuracy: 0.5000 - loss: 0.2541\n",
      "Epoch 288/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - accuracy: 0.5000 - loss: 0.2541\n",
      "Epoch 289/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - accuracy: 0.5000 - loss: 0.2541\n",
      "Epoch 290/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - accuracy: 0.5000 - loss: 0.2540\n",
      "Epoch 291/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - accuracy: 0.5000 - loss: 0.2540\n",
      "Epoch 292/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - accuracy: 0.5000 - loss: 0.2540\n",
      "Epoch 293/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - accuracy: 0.5000 - loss: 0.2540\n",
      "Epoch 294/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - accuracy: 0.5000 - loss: 0.2540\n",
      "Epoch 295/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - accuracy: 0.5000 - loss: 0.2539\n",
      "Epoch 296/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - accuracy: 0.5000 - loss: 0.2539\n",
      "Epoch 297/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - accuracy: 0.5000 - loss: 0.2539\n",
      "Epoch 298/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - accuracy: 0.5000 - loss: 0.2539\n",
      "Epoch 299/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - accuracy: 0.5000 - loss: 0.2539\n",
      "Epoch 300/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - accuracy: 0.5000 - loss: 0.2539\n",
      "Epoch 301/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - accuracy: 0.5000 - loss: 0.2538\n",
      "Epoch 302/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - accuracy: 0.5000 - loss: 0.2538\n",
      "Epoch 303/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - accuracy: 0.5000 - loss: 0.2538\n",
      "Epoch 304/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - accuracy: 0.5000 - loss: 0.2538\n",
      "Epoch 305/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - accuracy: 0.5000 - loss: 0.2538\n",
      "Epoch 306/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - accuracy: 0.5000 - loss: 0.2538\n",
      "Epoch 307/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - accuracy: 0.2500 - loss: 0.2537\n",
      "Epoch 308/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - accuracy: 0.2500 - loss: 0.2537\n",
      "Epoch 309/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - accuracy: 0.2500 - loss: 0.2537\n",
      "Epoch 310/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - accuracy: 0.2500 - loss: 0.2537\n",
      "Epoch 311/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - accuracy: 0.2500 - loss: 0.2537\n",
      "Epoch 312/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - accuracy: 0.2500 - loss: 0.2537\n",
      "Epoch 313/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - accuracy: 0.2500 - loss: 0.2536\n",
      "Epoch 314/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - accuracy: 0.2500 - loss: 0.2536\n",
      "Epoch 315/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - accuracy: 0.2500 - loss: 0.2536\n",
      "Epoch 316/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - accuracy: 0.2500 - loss: 0.2536\n",
      "Epoch 317/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - accuracy: 0.2500 - loss: 0.2536\n",
      "Epoch 318/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - accuracy: 0.2500 - loss: 0.2536\n",
      "Epoch 319/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - accuracy: 0.2500 - loss: 0.2536\n",
      "Epoch 320/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - accuracy: 0.2500 - loss: 0.2535\n",
      "Epoch 321/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - accuracy: 0.2500 - loss: 0.2535\n",
      "Epoch 322/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - accuracy: 0.2500 - loss: 0.2535\n",
      "Epoch 323/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - accuracy: 0.2500 - loss: 0.2535\n",
      "Epoch 324/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - accuracy: 0.2500 - loss: 0.2535\n",
      "Epoch 325/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - accuracy: 0.2500 - loss: 0.2535\n",
      "Epoch 326/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - accuracy: 0.2500 - loss: 0.2534\n",
      "Epoch 327/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - accuracy: 0.2500 - loss: 0.2534\n",
      "Epoch 328/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - accuracy: 0.2500 - loss: 0.2534\n",
      "Epoch 329/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - accuracy: 0.2500 - loss: 0.2534\n",
      "Epoch 330/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - accuracy: 0.2500 - loss: 0.2534\n",
      "Epoch 331/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - accuracy: 0.2500 - loss: 0.2534\n",
      "Epoch 332/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - accuracy: 0.2500 - loss: 0.2534\n",
      "Epoch 333/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 166ms/step - accuracy: 0.2500 - loss: 0.2533\n",
      "Epoch 334/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - accuracy: 0.2500 - loss: 0.2533\n",
      "Epoch 335/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - accuracy: 0.2500 - loss: 0.2533\n",
      "Epoch 336/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - accuracy: 0.2500 - loss: 0.2533\n",
      "Epoch 337/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - accuracy: 0.2500 - loss: 0.2533\n",
      "Epoch 338/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - accuracy: 0.2500 - loss: 0.2533\n",
      "Epoch 339/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - accuracy: 0.2500 - loss: 0.2533\n",
      "Epoch 340/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - accuracy: 0.2500 - loss: 0.2533\n",
      "Epoch 341/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - accuracy: 0.2500 - loss: 0.2532\n",
      "Epoch 342/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - accuracy: 0.2500 - loss: 0.2532\n",
      "Epoch 343/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - accuracy: 0.2500 - loss: 0.2532\n",
      "Epoch 344/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - accuracy: 0.2500 - loss: 0.2532\n",
      "Epoch 345/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - accuracy: 0.2500 - loss: 0.2532\n",
      "Epoch 346/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - accuracy: 0.2500 - loss: 0.2532\n",
      "Epoch 347/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - accuracy: 0.2500 - loss: 0.2532\n",
      "Epoch 348/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - accuracy: 0.2500 - loss: 0.2531\n",
      "Epoch 349/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - accuracy: 0.2500 - loss: 0.2531\n",
      "Epoch 350/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - accuracy: 0.2500 - loss: 0.2531\n",
      "Epoch 351/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - accuracy: 0.2500 - loss: 0.2531\n",
      "Epoch 352/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - accuracy: 0.2500 - loss: 0.2531\n",
      "Epoch 353/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - accuracy: 0.2500 - loss: 0.2531\n",
      "Epoch 354/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - accuracy: 0.2500 - loss: 0.2531\n",
      "Epoch 355/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - accuracy: 0.2500 - loss: 0.2531\n",
      "Epoch 356/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - accuracy: 0.2500 - loss: 0.2530\n",
      "Epoch 357/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - accuracy: 0.2500 - loss: 0.2530\n",
      "Epoch 358/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - accuracy: 0.2500 - loss: 0.2530\n",
      "Epoch 359/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - accuracy: 0.2500 - loss: 0.2530\n",
      "Epoch 360/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 136ms/step - accuracy: 0.2500 - loss: 0.2530\n",
      "Epoch 361/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - accuracy: 0.2500 - loss: 0.2530\n",
      "Epoch 362/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - accuracy: 0.2500 - loss: 0.2530\n",
      "Epoch 363/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - accuracy: 0.2500 - loss: 0.2530\n",
      "Epoch 364/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - accuracy: 0.2500 - loss: 0.2529\n",
      "Epoch 365/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - accuracy: 0.2500 - loss: 0.2529\n",
      "Epoch 366/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - accuracy: 0.2500 - loss: 0.2529\n",
      "Epoch 367/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - accuracy: 0.2500 - loss: 0.2529\n",
      "Epoch 368/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - accuracy: 0.2500 - loss: 0.2529\n",
      "Epoch 369/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - accuracy: 0.2500 - loss: 0.2529\n",
      "Epoch 370/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - accuracy: 0.2500 - loss: 0.2529\n",
      "Epoch 371/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - accuracy: 0.2500 - loss: 0.2529\n",
      "Epoch 372/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - accuracy: 0.2500 - loss: 0.2529\n",
      "Epoch 373/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - accuracy: 0.2500 - loss: 0.2528\n",
      "Epoch 374/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - accuracy: 0.2500 - loss: 0.2528\n",
      "Epoch 375/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - accuracy: 0.2500 - loss: 0.2528\n",
      "Epoch 376/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - accuracy: 0.2500 - loss: 0.2528\n",
      "Epoch 377/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89ms/step - accuracy: 0.2500 - loss: 0.2528\n",
      "Epoch 378/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - accuracy: 0.2500 - loss: 0.2528\n",
      "Epoch 379/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - accuracy: 0.2500 - loss: 0.2528\n",
      "Epoch 380/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - accuracy: 0.2500 - loss: 0.2528\n",
      "Epoch 381/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - accuracy: 0.2500 - loss: 0.2528\n",
      "Epoch 382/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - accuracy: 0.2500 - loss: 0.2527\n",
      "Epoch 383/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - accuracy: 0.2500 - loss: 0.2527\n",
      "Epoch 384/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - accuracy: 0.2500 - loss: 0.2527\n",
      "Epoch 385/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - accuracy: 0.2500 - loss: 0.2527\n",
      "Epoch 386/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - accuracy: 0.2500 - loss: 0.2527\n",
      "Epoch 387/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - accuracy: 0.2500 - loss: 0.2527\n",
      "Epoch 388/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - accuracy: 0.2500 - loss: 0.2527\n",
      "Epoch 389/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - accuracy: 0.2500 - loss: 0.2527\n",
      "Epoch 390/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - accuracy: 0.2500 - loss: 0.2527\n",
      "Epoch 391/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - accuracy: 0.2500 - loss: 0.2527\n",
      "Epoch 392/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - accuracy: 0.2500 - loss: 0.2526\n",
      "Epoch 393/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - accuracy: 0.2500 - loss: 0.2526\n",
      "Epoch 394/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - accuracy: 0.2500 - loss: 0.2526\n",
      "Epoch 395/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - accuracy: 0.2500 - loss: 0.2526\n",
      "Epoch 396/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - accuracy: 0.2500 - loss: 0.2526\n",
      "Epoch 397/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - accuracy: 0.2500 - loss: 0.2526\n",
      "Epoch 398/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step - accuracy: 0.2500 - loss: 0.2526\n",
      "Epoch 399/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - accuracy: 0.2500 - loss: 0.2526\n",
      "Epoch 400/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - accuracy: 0.2500 - loss: 0.2526\n",
      "Epoch 401/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - accuracy: 0.2500 - loss: 0.2526\n",
      "Epoch 402/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - accuracy: 0.2500 - loss: 0.2525\n",
      "Epoch 403/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - accuracy: 0.2500 - loss: 0.2525\n",
      "Epoch 404/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - accuracy: 0.2500 - loss: 0.2525\n",
      "Epoch 405/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - accuracy: 0.2500 - loss: 0.2525\n",
      "Epoch 406/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - accuracy: 0.2500 - loss: 0.2525\n",
      "Epoch 407/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - accuracy: 0.2500 - loss: 0.2525\n",
      "Epoch 408/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - accuracy: 0.2500 - loss: 0.2525\n",
      "Epoch 409/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - accuracy: 0.2500 - loss: 0.2525\n",
      "Epoch 410/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - accuracy: 0.2500 - loss: 0.2525\n",
      "Epoch 411/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - accuracy: 0.2500 - loss: 0.2525\n",
      "Epoch 412/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - accuracy: 0.2500 - loss: 0.2525\n",
      "Epoch 413/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - accuracy: 0.2500 - loss: 0.2524\n",
      "Epoch 414/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - accuracy: 0.2500 - loss: 0.2524\n",
      "Epoch 415/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - accuracy: 0.2500 - loss: 0.2524\n",
      "Epoch 416/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - accuracy: 0.2500 - loss: 0.2524\n",
      "Epoch 417/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - accuracy: 0.2500 - loss: 0.2524\n",
      "Epoch 418/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - accuracy: 0.2500 - loss: 0.2524\n",
      "Epoch 419/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - accuracy: 0.2500 - loss: 0.2524\n",
      "Epoch 420/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - accuracy: 0.2500 - loss: 0.2524\n",
      "Epoch 421/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - accuracy: 0.2500 - loss: 0.2524\n",
      "Epoch 422/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - accuracy: 0.2500 - loss: 0.2524\n",
      "Epoch 423/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - accuracy: 0.2500 - loss: 0.2524\n",
      "Epoch 424/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - accuracy: 0.2500 - loss: 0.2523\n",
      "Epoch 425/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - accuracy: 0.2500 - loss: 0.2523\n",
      "Epoch 426/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - accuracy: 0.2500 - loss: 0.2523\n",
      "Epoch 427/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - accuracy: 0.2500 - loss: 0.2523\n",
      "Epoch 428/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 132ms/step - accuracy: 0.2500 - loss: 0.2523\n",
      "Epoch 429/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - accuracy: 0.2500 - loss: 0.2523\n",
      "Epoch 430/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - accuracy: 0.2500 - loss: 0.2523\n",
      "Epoch 431/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - accuracy: 0.2500 - loss: 0.2523\n",
      "Epoch 432/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - accuracy: 0.2500 - loss: 0.2523\n",
      "Epoch 433/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - accuracy: 0.2500 - loss: 0.2523\n",
      "Epoch 434/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - accuracy: 0.2500 - loss: 0.2523\n",
      "Epoch 435/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - accuracy: 0.2500 - loss: 0.2523\n",
      "Epoch 436/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - accuracy: 0.2500 - loss: 0.2522\n",
      "Epoch 437/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - accuracy: 0.2500 - loss: 0.2522\n",
      "Epoch 438/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - accuracy: 0.2500 - loss: 0.2522\n",
      "Epoch 439/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - accuracy: 0.2500 - loss: 0.2522\n",
      "Epoch 440/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - accuracy: 0.2500 - loss: 0.2522\n",
      "Epoch 441/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - accuracy: 0.2500 - loss: 0.2522\n",
      "Epoch 442/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - accuracy: 0.2500 - loss: 0.2522\n",
      "Epoch 443/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - accuracy: 0.2500 - loss: 0.2522\n",
      "Epoch 444/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - accuracy: 0.2500 - loss: 0.2522\n",
      "Epoch 445/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - accuracy: 0.2500 - loss: 0.2522\n",
      "Epoch 446/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - accuracy: 0.2500 - loss: 0.2522\n",
      "Epoch 447/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - accuracy: 0.2500 - loss: 0.2522\n",
      "Epoch 448/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - accuracy: 0.2500 - loss: 0.2521\n",
      "Epoch 449/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - accuracy: 0.2500 - loss: 0.2521\n",
      "Epoch 450/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - accuracy: 0.2500 - loss: 0.2521\n",
      "Epoch 451/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step - accuracy: 0.2500 - loss: 0.2521\n",
      "Epoch 452/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - accuracy: 0.2500 - loss: 0.2521\n",
      "Epoch 453/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - accuracy: 0.2500 - loss: 0.2521\n",
      "Epoch 454/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - accuracy: 0.2500 - loss: 0.2521\n",
      "Epoch 455/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - accuracy: 0.2500 - loss: 0.2521\n",
      "Epoch 456/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - accuracy: 0.2500 - loss: 0.2521\n",
      "Epoch 457/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - accuracy: 0.2500 - loss: 0.2521\n",
      "Epoch 458/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - accuracy: 0.2500 - loss: 0.2521\n",
      "Epoch 459/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - accuracy: 0.2500 - loss: 0.2521\n",
      "Epoch 460/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - accuracy: 0.2500 - loss: 0.2521\n",
      "Epoch 461/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - accuracy: 0.2500 - loss: 0.2521\n",
      "Epoch 462/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - accuracy: 0.2500 - loss: 0.2520\n",
      "Epoch 463/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - accuracy: 0.2500 - loss: 0.2520\n",
      "Epoch 464/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - accuracy: 0.2500 - loss: 0.2520\n",
      "Epoch 465/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - accuracy: 0.2500 - loss: 0.2520\n",
      "Epoch 466/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - accuracy: 0.2500 - loss: 0.2520\n",
      "Epoch 467/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - accuracy: 0.2500 - loss: 0.2520\n",
      "Epoch 468/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - accuracy: 0.2500 - loss: 0.2520\n",
      "Epoch 469/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - accuracy: 0.2500 - loss: 0.2520\n",
      "Epoch 470/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - accuracy: 0.2500 - loss: 0.2520\n",
      "Epoch 471/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - accuracy: 0.2500 - loss: 0.2520\n",
      "Epoch 472/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - accuracy: 0.2500 - loss: 0.2520\n",
      "Epoch 473/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - accuracy: 0.2500 - loss: 0.2520\n",
      "Epoch 474/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - accuracy: 0.2500 - loss: 0.2520\n",
      "Epoch 475/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - accuracy: 0.2500 - loss: 0.2520\n",
      "Epoch 476/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - accuracy: 0.2500 - loss: 0.2519\n",
      "Epoch 477/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - accuracy: 0.2500 - loss: 0.2519\n",
      "Epoch 478/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - accuracy: 0.2500 - loss: 0.2519\n",
      "Epoch 479/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - accuracy: 0.2500 - loss: 0.2519\n",
      "Epoch 480/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - accuracy: 0.2500 - loss: 0.2519\n",
      "Epoch 481/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - accuracy: 0.2500 - loss: 0.2519\n",
      "Epoch 482/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - accuracy: 0.2500 - loss: 0.2519\n",
      "Epoch 483/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - accuracy: 0.2500 - loss: 0.2519\n",
      "Epoch 484/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - accuracy: 0.2500 - loss: 0.2519\n",
      "Epoch 485/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - accuracy: 0.2500 - loss: 0.2519\n",
      "Epoch 486/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - accuracy: 0.2500 - loss: 0.2519\n",
      "Epoch 487/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - accuracy: 0.2500 - loss: 0.2519\n",
      "Epoch 488/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - accuracy: 0.2500 - loss: 0.2519\n",
      "Epoch 489/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - accuracy: 0.2500 - loss: 0.2519\n",
      "Epoch 490/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - accuracy: 0.2500 - loss: 0.2519\n",
      "Epoch 491/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - accuracy: 0.2500 - loss: 0.2519\n",
      "Epoch 492/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - accuracy: 0.2500 - loss: 0.2518\n",
      "Epoch 493/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - accuracy: 0.2500 - loss: 0.2518\n",
      "Epoch 494/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - accuracy: 0.2500 - loss: 0.2518\n",
      "Epoch 495/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - accuracy: 0.2500 - loss: 0.2518\n",
      "Epoch 496/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - accuracy: 0.2500 - loss: 0.2518\n",
      "Epoch 497/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - accuracy: 0.2500 - loss: 0.2518\n",
      "Epoch 498/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - accuracy: 0.2500 - loss: 0.2518\n",
      "Epoch 499/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - accuracy: 0.2500 - loss: 0.2518\n",
      "Epoch 500/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - accuracy: 0.2500 - loss: 0.2518\n",
      "Epoch 501/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - accuracy: 0.2500 - loss: 0.2518\n",
      "Epoch 502/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - accuracy: 0.2500 - loss: 0.2518\n",
      "Epoch 503/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - accuracy: 0.2500 - loss: 0.2518\n",
      "Epoch 504/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 114ms/step - accuracy: 0.2500 - loss: 0.2518\n",
      "Epoch 505/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - accuracy: 0.2500 - loss: 0.2518\n",
      "Epoch 506/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - accuracy: 0.2500 - loss: 0.2518\n",
      "Epoch 507/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - accuracy: 0.2500 - loss: 0.2518\n",
      "Epoch 508/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - accuracy: 0.2500 - loss: 0.2517\n",
      "Epoch 509/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - accuracy: 0.2500 - loss: 0.2517\n",
      "Epoch 510/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - accuracy: 0.2500 - loss: 0.2517\n",
      "Epoch 511/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - accuracy: 0.2500 - loss: 0.2517\n",
      "Epoch 512/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - accuracy: 0.2500 - loss: 0.2517\n",
      "Epoch 513/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - accuracy: 0.2500 - loss: 0.2517\n",
      "Epoch 514/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - accuracy: 0.2500 - loss: 0.2517\n",
      "Epoch 515/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - accuracy: 0.2500 - loss: 0.2517\n",
      "Epoch 516/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - accuracy: 0.2500 - loss: 0.2517\n",
      "Epoch 517/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - accuracy: 0.2500 - loss: 0.2517\n",
      "Epoch 518/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - accuracy: 0.2500 - loss: 0.2517\n",
      "Epoch 519/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - accuracy: 0.2500 - loss: 0.2517\n",
      "Epoch 520/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - accuracy: 0.2500 - loss: 0.2517\n",
      "Epoch 521/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - accuracy: 0.2500 - loss: 0.2517\n",
      "Epoch 522/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - accuracy: 0.2500 - loss: 0.2517\n",
      "Epoch 523/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - accuracy: 0.2500 - loss: 0.2517\n",
      "Epoch 524/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - accuracy: 0.2500 - loss: 0.2517\n",
      "Epoch 525/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - accuracy: 0.2500 - loss: 0.2517\n",
      "Epoch 526/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - accuracy: 0.2500 - loss: 0.2516\n",
      "Epoch 527/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - accuracy: 0.2500 - loss: 0.2516\n",
      "Epoch 528/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - accuracy: 0.2500 - loss: 0.2516\n",
      "Epoch 529/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 214ms/step - accuracy: 0.2500 - loss: 0.2516\n",
      "Epoch 530/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - accuracy: 0.2500 - loss: 0.2516\n",
      "Epoch 531/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - accuracy: 0.2500 - loss: 0.2516\n",
      "Epoch 532/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - accuracy: 0.2500 - loss: 0.2516\n",
      "Epoch 533/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - accuracy: 0.2500 - loss: 0.2516\n",
      "Epoch 534/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - accuracy: 0.2500 - loss: 0.2516\n",
      "Epoch 535/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - accuracy: 0.2500 - loss: 0.2516\n",
      "Epoch 536/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - accuracy: 0.2500 - loss: 0.2516\n",
      "Epoch 537/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - accuracy: 0.2500 - loss: 0.2516\n",
      "Epoch 538/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - accuracy: 0.2500 - loss: 0.2516\n",
      "Epoch 539/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - accuracy: 0.2500 - loss: 0.2516\n",
      "Epoch 540/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - accuracy: 0.2500 - loss: 0.2516\n",
      "Epoch 541/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85ms/step - accuracy: 0.2500 - loss: 0.2516\n",
      "Epoch 542/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - accuracy: 0.2500 - loss: 0.2516\n",
      "Epoch 543/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - accuracy: 0.2500 - loss: 0.2516\n",
      "Epoch 544/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - accuracy: 0.2500 - loss: 0.2516\n",
      "Epoch 545/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - accuracy: 0.2500 - loss: 0.2516\n",
      "Epoch 546/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - accuracy: 0.2500 - loss: 0.2515\n",
      "Epoch 547/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - accuracy: 0.2500 - loss: 0.2515\n",
      "Epoch 548/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - accuracy: 0.2500 - loss: 0.2515\n",
      "Epoch 549/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - accuracy: 0.2500 - loss: 0.2515\n",
      "Epoch 550/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - accuracy: 0.2500 - loss: 0.2515\n",
      "Epoch 551/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - accuracy: 0.2500 - loss: 0.2515\n",
      "Epoch 552/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - accuracy: 0.2500 - loss: 0.2515\n",
      "Epoch 553/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - accuracy: 0.2500 - loss: 0.2515\n",
      "Epoch 554/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - accuracy: 0.2500 - loss: 0.2515\n",
      "Epoch 555/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - accuracy: 0.2500 - loss: 0.2515\n",
      "Epoch 556/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - accuracy: 0.2500 - loss: 0.2515\n",
      "Epoch 557/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - accuracy: 0.2500 - loss: 0.2515\n",
      "Epoch 558/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - accuracy: 0.2500 - loss: 0.2515\n",
      "Epoch 559/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - accuracy: 0.2500 - loss: 0.2515\n",
      "Epoch 560/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - accuracy: 0.2500 - loss: 0.2515\n",
      "Epoch 561/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - accuracy: 0.2500 - loss: 0.2515\n",
      "Epoch 562/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - accuracy: 0.2500 - loss: 0.2515\n",
      "Epoch 563/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - accuracy: 0.2500 - loss: 0.2515\n",
      "Epoch 564/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - accuracy: 0.2500 - loss: 0.2515\n",
      "Epoch 565/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - accuracy: 0.2500 - loss: 0.2515\n",
      "Epoch 566/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - accuracy: 0.2500 - loss: 0.2515\n",
      "Epoch 567/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - accuracy: 0.2500 - loss: 0.2514\n",
      "Epoch 568/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - accuracy: 0.2500 - loss: 0.2514\n",
      "Epoch 569/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - accuracy: 0.2500 - loss: 0.2514\n",
      "Epoch 570/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - accuracy: 0.2500 - loss: 0.2514\n",
      "Epoch 571/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - accuracy: 0.2500 - loss: 0.2514\n",
      "Epoch 572/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - accuracy: 0.2500 - loss: 0.2514\n",
      "Epoch 573/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - accuracy: 0.2500 - loss: 0.2514\n",
      "Epoch 574/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 144ms/step - accuracy: 0.2500 - loss: 0.2514\n",
      "Epoch 575/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - accuracy: 0.2500 - loss: 0.2514\n",
      "Epoch 576/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - accuracy: 0.2500 - loss: 0.2514\n",
      "Epoch 577/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - accuracy: 0.2500 - loss: 0.2514\n",
      "Epoch 578/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - accuracy: 0.2500 - loss: 0.2514\n",
      "Epoch 579/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - accuracy: 0.2500 - loss: 0.2514\n",
      "Epoch 580/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - accuracy: 0.2500 - loss: 0.2514\n",
      "Epoch 581/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - accuracy: 0.2500 - loss: 0.2514\n",
      "Epoch 582/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - accuracy: 0.2500 - loss: 0.2514\n",
      "Epoch 583/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - accuracy: 0.2500 - loss: 0.2514\n",
      "Epoch 584/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - accuracy: 0.2500 - loss: 0.2514\n",
      "Epoch 585/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - accuracy: 0.2500 - loss: 0.2514\n",
      "Epoch 586/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - accuracy: 0.2500 - loss: 0.2514\n",
      "Epoch 587/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - accuracy: 0.2500 - loss: 0.2514\n",
      "Epoch 588/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - accuracy: 0.2500 - loss: 0.2514\n",
      "Epoch 589/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - accuracy: 0.2500 - loss: 0.2514\n",
      "Epoch 590/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - accuracy: 0.2500 - loss: 0.2514\n",
      "Epoch 591/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - accuracy: 0.2500 - loss: 0.2513\n",
      "Epoch 592/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - accuracy: 0.2500 - loss: 0.2513\n",
      "Epoch 593/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - accuracy: 0.2500 - loss: 0.2513\n",
      "Epoch 594/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - accuracy: 0.2500 - loss: 0.2513\n",
      "Epoch 595/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - accuracy: 0.2500 - loss: 0.2513\n",
      "Epoch 596/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - accuracy: 0.2500 - loss: 0.2513\n",
      "Epoch 597/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - accuracy: 0.2500 - loss: 0.2513\n",
      "Epoch 598/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - accuracy: 0.2500 - loss: 0.2513\n",
      "Epoch 599/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - accuracy: 0.2500 - loss: 0.2513\n",
      "Epoch 600/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - accuracy: 0.2500 - loss: 0.2513\n",
      "Epoch 601/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - accuracy: 0.2500 - loss: 0.2513\n",
      "Epoch 602/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - accuracy: 0.2500 - loss: 0.2513\n",
      "Epoch 603/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - accuracy: 0.2500 - loss: 0.2513\n",
      "Epoch 604/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - accuracy: 0.2500 - loss: 0.2513\n",
      "Epoch 605/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - accuracy: 0.2500 - loss: 0.2513\n",
      "Epoch 606/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - accuracy: 0.2500 - loss: 0.2513\n",
      "Epoch 607/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - accuracy: 0.2500 - loss: 0.2513\n",
      "Epoch 608/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - accuracy: 0.2500 - loss: 0.2513\n",
      "Epoch 609/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - accuracy: 0.2500 - loss: 0.2513\n",
      "Epoch 610/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - accuracy: 0.2500 - loss: 0.2513\n",
      "Epoch 611/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - accuracy: 0.2500 - loss: 0.2513\n",
      "Epoch 612/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - accuracy: 0.2500 - loss: 0.2513\n",
      "Epoch 613/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 135ms/step - accuracy: 0.2500 - loss: 0.2513\n",
      "Epoch 614/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - accuracy: 0.2500 - loss: 0.2513\n",
      "Epoch 615/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - accuracy: 0.2500 - loss: 0.2513\n",
      "Epoch 616/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - accuracy: 0.2500 - loss: 0.2512\n",
      "Epoch 617/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - accuracy: 0.2500 - loss: 0.2512\n",
      "Epoch 618/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - accuracy: 0.2500 - loss: 0.2512\n",
      "Epoch 619/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - accuracy: 0.2500 - loss: 0.2512\n",
      "Epoch 620/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - accuracy: 0.2500 - loss: 0.2512\n",
      "Epoch 621/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - accuracy: 0.2500 - loss: 0.2512\n",
      "Epoch 622/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - accuracy: 0.2500 - loss: 0.2512\n",
      "Epoch 623/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - accuracy: 0.2500 - loss: 0.2512\n",
      "Epoch 624/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - accuracy: 0.2500 - loss: 0.2512\n",
      "Epoch 625/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - accuracy: 0.2500 - loss: 0.2512\n",
      "Epoch 626/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - accuracy: 0.2500 - loss: 0.2512\n",
      "Epoch 627/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - accuracy: 0.2500 - loss: 0.2512\n",
      "Epoch 628/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - accuracy: 0.2500 - loss: 0.2512\n",
      "Epoch 629/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - accuracy: 0.2500 - loss: 0.2512\n",
      "Epoch 630/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - accuracy: 0.2500 - loss: 0.2512\n",
      "Epoch 631/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - accuracy: 0.2500 - loss: 0.2512\n",
      "Epoch 632/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 108ms/step - accuracy: 0.2500 - loss: 0.2512\n",
      "Epoch 633/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - accuracy: 0.2500 - loss: 0.2512\n",
      "Epoch 634/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - accuracy: 0.2500 - loss: 0.2512\n",
      "Epoch 635/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - accuracy: 0.2500 - loss: 0.2512\n",
      "Epoch 636/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - accuracy: 0.2500 - loss: 0.2512\n",
      "Epoch 637/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - accuracy: 0.2500 - loss: 0.2512\n",
      "Epoch 638/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - accuracy: 0.2500 - loss: 0.2512\n",
      "Epoch 639/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - accuracy: 0.2500 - loss: 0.2512\n",
      "Epoch 640/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - accuracy: 0.2500 - loss: 0.2512\n",
      "Epoch 641/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - accuracy: 0.2500 - loss: 0.2512\n",
      "Epoch 642/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - accuracy: 0.2500 - loss: 0.2512\n",
      "Epoch 643/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - accuracy: 0.2500 - loss: 0.2512\n",
      "Epoch 644/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - accuracy: 0.2500 - loss: 0.2512\n",
      "Epoch 645/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - accuracy: 0.2500 - loss: 0.2511\n",
      "Epoch 646/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - accuracy: 0.2500 - loss: 0.2511\n",
      "Epoch 647/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - accuracy: 0.2500 - loss: 0.2511\n",
      "Epoch 648/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - accuracy: 0.2500 - loss: 0.2511\n",
      "Epoch 649/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - accuracy: 0.2500 - loss: 0.2511\n",
      "Epoch 650/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - accuracy: 0.2500 - loss: 0.2511\n",
      "Epoch 651/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 86ms/step - accuracy: 0.2500 - loss: 0.2511\n",
      "Epoch 652/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - accuracy: 0.2500 - loss: 0.2511\n",
      "Epoch 653/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - accuracy: 0.2500 - loss: 0.2511\n",
      "Epoch 654/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - accuracy: 0.2500 - loss: 0.2511\n",
      "Epoch 655/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - accuracy: 0.2500 - loss: 0.2511\n",
      "Epoch 656/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - accuracy: 0.2500 - loss: 0.2511\n",
      "Epoch 657/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - accuracy: 0.2500 - loss: 0.2511\n",
      "Epoch 658/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - accuracy: 0.2500 - loss: 0.2511\n",
      "Epoch 659/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - accuracy: 0.2500 - loss: 0.2511\n",
      "Epoch 660/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - accuracy: 0.2500 - loss: 0.2511\n",
      "Epoch 661/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - accuracy: 0.2500 - loss: 0.2511\n",
      "Epoch 662/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - accuracy: 0.2500 - loss: 0.2511\n",
      "Epoch 663/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - accuracy: 0.2500 - loss: 0.2511\n",
      "Epoch 664/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - accuracy: 0.2500 - loss: 0.2511\n",
      "Epoch 665/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - accuracy: 0.2500 - loss: 0.2511\n",
      "Epoch 666/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - accuracy: 0.2500 - loss: 0.2511\n",
      "Epoch 667/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - accuracy: 0.2500 - loss: 0.2511\n",
      "Epoch 668/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - accuracy: 0.2500 - loss: 0.2511\n",
      "Epoch 669/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - accuracy: 0.2500 - loss: 0.2511\n",
      "Epoch 670/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 124ms/step - accuracy: 0.2500 - loss: 0.2511\n",
      "Epoch 671/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - accuracy: 0.2500 - loss: 0.2511\n",
      "Epoch 672/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - accuracy: 0.2500 - loss: 0.2511\n",
      "Epoch 673/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - accuracy: 0.2500 - loss: 0.2511\n",
      "Epoch 674/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - accuracy: 0.2500 - loss: 0.2511\n",
      "Epoch 675/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - accuracy: 0.2500 - loss: 0.2511\n",
      "Epoch 676/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - accuracy: 0.2500 - loss: 0.2511\n",
      "Epoch 677/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - accuracy: 0.2500 - loss: 0.2510\n",
      "Epoch 678/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - accuracy: 0.2500 - loss: 0.2510\n",
      "Epoch 679/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - accuracy: 0.2500 - loss: 0.2510\n",
      "Epoch 680/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - accuracy: 0.2500 - loss: 0.2510\n",
      "Epoch 681/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - accuracy: 0.2500 - loss: 0.2510\n",
      "Epoch 682/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - accuracy: 0.2500 - loss: 0.2510\n",
      "Epoch 683/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - accuracy: 0.2500 - loss: 0.2510\n",
      "Epoch 684/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - accuracy: 0.2500 - loss: 0.2510\n",
      "Epoch 685/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - accuracy: 0.2500 - loss: 0.2510\n",
      "Epoch 686/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - accuracy: 0.2500 - loss: 0.2510\n",
      "Epoch 687/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 97ms/step - accuracy: 0.2500 - loss: 0.2510\n",
      "Epoch 688/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - accuracy: 0.2500 - loss: 0.2510\n",
      "Epoch 689/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - accuracy: 0.2500 - loss: 0.2510\n",
      "Epoch 690/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - accuracy: 0.2500 - loss: 0.2510\n",
      "Epoch 691/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - accuracy: 0.2500 - loss: 0.2510\n",
      "Epoch 692/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - accuracy: 0.2500 - loss: 0.2510\n",
      "Epoch 693/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - accuracy: 0.2500 - loss: 0.2510\n",
      "Epoch 694/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - accuracy: 0.2500 - loss: 0.2510\n",
      "Epoch 695/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - accuracy: 0.2500 - loss: 0.2510\n",
      "Epoch 696/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - accuracy: 0.2500 - loss: 0.2510\n",
      "Epoch 697/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - accuracy: 0.2500 - loss: 0.2510\n",
      "Epoch 698/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - accuracy: 0.2500 - loss: 0.2510\n",
      "Epoch 699/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - accuracy: 0.2500 - loss: 0.2510\n",
      "Epoch 700/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - accuracy: 0.2500 - loss: 0.2510\n",
      "Epoch 701/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - accuracy: 0.2500 - loss: 0.2510\n",
      "Epoch 702/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - accuracy: 0.2500 - loss: 0.2510\n",
      "Epoch 703/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - accuracy: 0.2500 - loss: 0.2510\n",
      "Epoch 704/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - accuracy: 0.2500 - loss: 0.2510\n",
      "Epoch 705/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - accuracy: 0.2500 - loss: 0.2510\n",
      "Epoch 706/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - accuracy: 0.2500 - loss: 0.2510\n",
      "Epoch 707/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - accuracy: 0.2500 - loss: 0.2510\n",
      "Epoch 708/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - accuracy: 0.2500 - loss: 0.2510\n",
      "Epoch 709/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - accuracy: 0.2500 - loss: 0.2510\n",
      "Epoch 710/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - accuracy: 0.2500 - loss: 0.2510\n",
      "Epoch 711/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - accuracy: 0.2500 - loss: 0.2510\n",
      "Epoch 712/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - accuracy: 0.2500 - loss: 0.2510\n",
      "Epoch 713/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - accuracy: 0.2500 - loss: 0.2510\n",
      "Epoch 714/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - accuracy: 0.2500 - loss: 0.2509\n",
      "Epoch 715/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - accuracy: 0.2500 - loss: 0.2509\n",
      "Epoch 716/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - accuracy: 0.2500 - loss: 0.2509\n",
      "Epoch 717/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - accuracy: 0.2500 - loss: 0.2509\n",
      "Epoch 718/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - accuracy: 0.2500 - loss: 0.2509\n",
      "Epoch 719/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - accuracy: 0.2500 - loss: 0.2509\n",
      "Epoch 720/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - accuracy: 0.2500 - loss: 0.2509\n",
      "Epoch 721/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - accuracy: 0.2500 - loss: 0.2509\n",
      "Epoch 722/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 126ms/step - accuracy: 0.2500 - loss: 0.2509\n",
      "Epoch 723/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - accuracy: 0.2500 - loss: 0.2509\n",
      "Epoch 724/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - accuracy: 0.2500 - loss: 0.2509\n",
      "Epoch 725/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - accuracy: 0.2500 - loss: 0.2509\n",
      "Epoch 726/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - accuracy: 0.2500 - loss: 0.2509\n",
      "Epoch 727/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - accuracy: 0.2500 - loss: 0.2509\n",
      "Epoch 728/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - accuracy: 0.2500 - loss: 0.2509\n",
      "Epoch 729/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - accuracy: 0.2500 - loss: 0.2509\n",
      "Epoch 730/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - accuracy: 0.2500 - loss: 0.2509\n",
      "Epoch 731/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - accuracy: 0.2500 - loss: 0.2509\n",
      "Epoch 732/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - accuracy: 0.2500 - loss: 0.2509\n",
      "Epoch 733/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - accuracy: 0.2500 - loss: 0.2509\n",
      "Epoch 734/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - accuracy: 0.2500 - loss: 0.2509\n",
      "Epoch 735/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - accuracy: 0.2500 - loss: 0.2509\n",
      "Epoch 736/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - accuracy: 0.2500 - loss: 0.2509\n",
      "Epoch 737/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - accuracy: 0.2500 - loss: 0.2509\n",
      "Epoch 738/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - accuracy: 0.2500 - loss: 0.2509\n",
      "Epoch 739/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 133ms/step - accuracy: 0.2500 - loss: 0.2509\n",
      "Epoch 740/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - accuracy: 0.2500 - loss: 0.2509\n",
      "Epoch 741/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - accuracy: 0.2500 - loss: 0.2509\n",
      "Epoch 742/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - accuracy: 0.2500 - loss: 0.2509\n",
      "Epoch 743/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - accuracy: 0.2500 - loss: 0.2509\n",
      "Epoch 744/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - accuracy: 0.2500 - loss: 0.2509\n",
      "Epoch 745/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - accuracy: 0.2500 - loss: 0.2509\n",
      "Epoch 746/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - accuracy: 0.2500 - loss: 0.2509\n",
      "Epoch 747/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - accuracy: 0.2500 - loss: 0.2509\n",
      "Epoch 748/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - accuracy: 0.2500 - loss: 0.2509\n",
      "Epoch 749/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - accuracy: 0.2500 - loss: 0.2509\n",
      "Epoch 750/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - accuracy: 0.2500 - loss: 0.2509\n",
      "Epoch 751/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - accuracy: 0.2500 - loss: 0.2509\n",
      "Epoch 752/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - accuracy: 0.2500 - loss: 0.2509\n",
      "Epoch 753/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - accuracy: 0.2500 - loss: 0.2509\n",
      "Epoch 754/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - accuracy: 0.2500 - loss: 0.2509\n",
      "Epoch 755/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/step - accuracy: 0.2500 - loss: 0.2509\n",
      "Epoch 756/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - accuracy: 0.2500 - loss: 0.2508\n",
      "Epoch 757/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - accuracy: 0.2500 - loss: 0.2508\n",
      "Epoch 758/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - accuracy: 0.2500 - loss: 0.2508\n",
      "Epoch 759/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - accuracy: 0.2500 - loss: 0.2508\n",
      "Epoch 760/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - accuracy: 0.2500 - loss: 0.2508\n",
      "Epoch 761/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - accuracy: 0.2500 - loss: 0.2508\n",
      "Epoch 762/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - accuracy: 0.2500 - loss: 0.2508\n",
      "Epoch 763/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - accuracy: 0.2500 - loss: 0.2508\n",
      "Epoch 764/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - accuracy: 0.2500 - loss: 0.2508\n",
      "Epoch 765/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - accuracy: 0.2500 - loss: 0.2508\n",
      "Epoch 766/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - accuracy: 0.2500 - loss: 0.2508\n",
      "Epoch 767/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - accuracy: 0.2500 - loss: 0.2508\n",
      "Epoch 768/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - accuracy: 0.2500 - loss: 0.2508\n",
      "Epoch 769/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - accuracy: 0.2500 - loss: 0.2508\n",
      "Epoch 770/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - accuracy: 0.2500 - loss: 0.2508\n",
      "Epoch 771/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - accuracy: 0.2500 - loss: 0.2508\n",
      "Epoch 772/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 167ms/step - accuracy: 0.2500 - loss: 0.2508\n",
      "Epoch 773/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - accuracy: 0.2500 - loss: 0.2508\n",
      "Epoch 774/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - accuracy: 0.2500 - loss: 0.2508\n",
      "Epoch 775/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - accuracy: 0.2500 - loss: 0.2508\n",
      "Epoch 776/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - accuracy: 0.2500 - loss: 0.2508\n",
      "Epoch 777/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - accuracy: 0.2500 - loss: 0.2508\n",
      "Epoch 778/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - accuracy: 0.2500 - loss: 0.2508\n",
      "Epoch 779/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - accuracy: 0.2500 - loss: 0.2508\n",
      "Epoch 780/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - accuracy: 0.2500 - loss: 0.2508\n",
      "Epoch 781/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - accuracy: 0.2500 - loss: 0.2508\n",
      "Epoch 782/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - accuracy: 0.2500 - loss: 0.2508\n",
      "Epoch 783/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - accuracy: 0.2500 - loss: 0.2508\n",
      "Epoch 784/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - accuracy: 0.2500 - loss: 0.2508\n",
      "Epoch 785/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - accuracy: 0.2500 - loss: 0.2508\n",
      "Epoch 786/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - accuracy: 0.2500 - loss: 0.2508\n",
      "Epoch 787/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - accuracy: 0.2500 - loss: 0.2508\n",
      "Epoch 788/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 167ms/step - accuracy: 0.2500 - loss: 0.2508\n",
      "Epoch 789/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - accuracy: 0.2500 - loss: 0.2508\n",
      "Epoch 790/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - accuracy: 0.2500 - loss: 0.2508\n",
      "Epoch 791/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - accuracy: 0.2500 - loss: 0.2508\n",
      "Epoch 792/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - accuracy: 0.2500 - loss: 0.2508\n",
      "Epoch 793/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - accuracy: 0.2500 - loss: 0.2508\n",
      "Epoch 794/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - accuracy: 0.2500 - loss: 0.2508\n",
      "Epoch 795/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - accuracy: 0.2500 - loss: 0.2508\n",
      "Epoch 796/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - accuracy: 0.2500 - loss: 0.2508\n",
      "Epoch 797/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - accuracy: 0.2500 - loss: 0.2508\n",
      "Epoch 798/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - accuracy: 0.2500 - loss: 0.2508\n",
      "Epoch 799/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - accuracy: 0.2500 - loss: 0.2508\n",
      "Epoch 800/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - accuracy: 0.2500 - loss: 0.2508\n",
      "Epoch 801/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - accuracy: 0.2500 - loss: 0.2508\n",
      "Epoch 802/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - accuracy: 0.2500 - loss: 0.2508\n",
      "Epoch 803/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 101ms/step - accuracy: 0.2500 - loss: 0.2508\n",
      "Epoch 804/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 106ms/step - accuracy: 0.2500 - loss: 0.2508\n",
      "Epoch 805/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - accuracy: 0.2500 - loss: 0.2507\n",
      "Epoch 806/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - accuracy: 0.2500 - loss: 0.2507\n",
      "Epoch 807/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - accuracy: 0.2500 - loss: 0.2507\n",
      "Epoch 808/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - accuracy: 0.2500 - loss: 0.2507\n",
      "Epoch 809/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - accuracy: 0.2500 - loss: 0.2507\n",
      "Epoch 810/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - accuracy: 0.2500 - loss: 0.2507\n",
      "Epoch 811/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - accuracy: 0.2500 - loss: 0.2507\n",
      "Epoch 812/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 102ms/step - accuracy: 0.2500 - loss: 0.2507\n",
      "Epoch 813/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 98ms/step - accuracy: 0.2500 - loss: 0.2507\n",
      "Epoch 814/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 93ms/step - accuracy: 0.2500 - loss: 0.2507\n",
      "Epoch 815/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - accuracy: 0.2500 - loss: 0.2507\n",
      "Epoch 816/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85ms/step - accuracy: 0.2500 - loss: 0.2507\n",
      "Epoch 817/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - accuracy: 0.2500 - loss: 0.2507\n",
      "Epoch 818/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - accuracy: 0.2500 - loss: 0.2507\n",
      "Epoch 819/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 183ms/step - accuracy: 0.2500 - loss: 0.2507\n",
      "Epoch 820/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - accuracy: 0.2500 - loss: 0.2507\n",
      "Epoch 821/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - accuracy: 0.2500 - loss: 0.2507\n",
      "Epoch 822/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - accuracy: 0.2500 - loss: 0.2507\n",
      "Epoch 823/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - accuracy: 0.2500 - loss: 0.2507\n",
      "Epoch 824/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - accuracy: 0.2500 - loss: 0.2507\n",
      "Epoch 825/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - accuracy: 0.2500 - loss: 0.2507\n",
      "Epoch 826/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - accuracy: 0.2500 - loss: 0.2507\n",
      "Epoch 827/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - accuracy: 0.2500 - loss: 0.2507\n",
      "Epoch 828/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - accuracy: 0.2500 - loss: 0.2507\n",
      "Epoch 829/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - accuracy: 0.2500 - loss: 0.2507\n",
      "Epoch 830/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - accuracy: 0.2500 - loss: 0.2507\n",
      "Epoch 831/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - accuracy: 0.2500 - loss: 0.2507\n",
      "Epoch 832/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - accuracy: 0.2500 - loss: 0.2507\n",
      "Epoch 833/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 158ms/step - accuracy: 0.2500 - loss: 0.2507\n",
      "Epoch 834/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - accuracy: 0.2500 - loss: 0.2507\n",
      "Epoch 835/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - accuracy: 0.2500 - loss: 0.2507\n",
      "Epoch 836/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - accuracy: 0.2500 - loss: 0.2507\n",
      "Epoch 837/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - accuracy: 0.2500 - loss: 0.2507\n",
      "Epoch 838/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - accuracy: 0.2500 - loss: 0.2507\n",
      "Epoch 839/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - accuracy: 0.2500 - loss: 0.2507\n",
      "Epoch 840/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - accuracy: 0.2500 - loss: 0.2507\n",
      "Epoch 841/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - accuracy: 0.2500 - loss: 0.2507\n",
      "Epoch 842/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - accuracy: 0.2500 - loss: 0.2507\n",
      "Epoch 843/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - accuracy: 0.2500 - loss: 0.2507\n",
      "Epoch 844/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - accuracy: 0.2500 - loss: 0.2507\n",
      "Epoch 845/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - accuracy: 0.2500 - loss: 0.2507\n",
      "Epoch 846/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - accuracy: 0.2500 - loss: 0.2507\n",
      "Epoch 847/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 119ms/step - accuracy: 0.2500 - loss: 0.2507\n",
      "Epoch 848/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 90ms/step - accuracy: 0.2500 - loss: 0.2507\n",
      "Epoch 849/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - accuracy: 0.2500 - loss: 0.2507\n",
      "Epoch 850/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - accuracy: 0.2500 - loss: 0.2507\n",
      "Epoch 851/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - accuracy: 0.2500 - loss: 0.2507\n",
      "Epoch 852/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - accuracy: 0.2500 - loss: 0.2507\n",
      "Epoch 853/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - accuracy: 0.2500 - loss: 0.2507\n",
      "Epoch 854/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - accuracy: 0.2500 - loss: 0.2507\n",
      "Epoch 855/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - accuracy: 0.2500 - loss: 0.2507\n",
      "Epoch 856/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - accuracy: 0.2500 - loss: 0.2507\n",
      "Epoch 857/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - accuracy: 0.2500 - loss: 0.2507\n",
      "Epoch 858/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - accuracy: 0.2500 - loss: 0.2507\n",
      "Epoch 859/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - accuracy: 0.2500 - loss: 0.2507\n",
      "Epoch 860/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - accuracy: 0.2500 - loss: 0.2507\n",
      "Epoch 861/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - accuracy: 0.2500 - loss: 0.2507\n",
      "Epoch 862/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 119ms/step - accuracy: 0.2500 - loss: 0.2507\n",
      "Epoch 863/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - accuracy: 0.2500 - loss: 0.2506\n",
      "Epoch 864/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - accuracy: 0.2500 - loss: 0.2506\n",
      "Epoch 865/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - accuracy: 0.2500 - loss: 0.2506\n",
      "Epoch 866/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - accuracy: 0.2500 - loss: 0.2506\n",
      "Epoch 867/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - accuracy: 0.2500 - loss: 0.2506\n",
      "Epoch 868/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - accuracy: 0.2500 - loss: 0.2506\n",
      "Epoch 869/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - accuracy: 0.2500 - loss: 0.2506\n",
      "Epoch 870/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - accuracy: 0.2500 - loss: 0.2506\n",
      "Epoch 871/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - accuracy: 0.2500 - loss: 0.2506\n",
      "Epoch 872/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - accuracy: 0.2500 - loss: 0.2506\n",
      "Epoch 873/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - accuracy: 0.2500 - loss: 0.2506\n",
      "Epoch 874/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - accuracy: 0.2500 - loss: 0.2506\n",
      "Epoch 875/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - accuracy: 0.2500 - loss: 0.2506\n",
      "Epoch 876/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - accuracy: 0.2500 - loss: 0.2506\n",
      "Epoch 877/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 119ms/step - accuracy: 0.2500 - loss: 0.2506\n",
      "Epoch 878/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - accuracy: 0.2500 - loss: 0.2506\n",
      "Epoch 879/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - accuracy: 0.2500 - loss: 0.2506\n",
      "Epoch 880/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - accuracy: 0.2500 - loss: 0.2506\n",
      "Epoch 881/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - accuracy: 0.2500 - loss: 0.2506\n",
      "Epoch 882/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - accuracy: 0.2500 - loss: 0.2506\n",
      "Epoch 883/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - accuracy: 0.2500 - loss: 0.2506\n",
      "Epoch 884/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - accuracy: 0.2500 - loss: 0.2506\n",
      "Epoch 885/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - accuracy: 0.2500 - loss: 0.2506\n",
      "Epoch 886/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - accuracy: 0.2500 - loss: 0.2506\n",
      "Epoch 887/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - accuracy: 0.2500 - loss: 0.2506\n",
      "Epoch 888/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - accuracy: 0.2500 - loss: 0.2506\n",
      "Epoch 889/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - accuracy: 0.2500 - loss: 0.2506\n",
      "Epoch 890/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - accuracy: 0.2500 - loss: 0.2506\n",
      "Epoch 891/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 144ms/step - accuracy: 0.2500 - loss: 0.2506\n",
      "Epoch 892/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - accuracy: 0.2500 - loss: 0.2506\n",
      "Epoch 893/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - accuracy: 0.2500 - loss: 0.2506\n",
      "Epoch 894/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - accuracy: 0.2500 - loss: 0.2506\n",
      "Epoch 895/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - accuracy: 0.2500 - loss: 0.2506\n",
      "Epoch 896/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - accuracy: 0.2500 - loss: 0.2506\n",
      "Epoch 897/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - accuracy: 0.2500 - loss: 0.2506\n",
      "Epoch 898/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - accuracy: 0.2500 - loss: 0.2506\n",
      "Epoch 899/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - accuracy: 0.2500 - loss: 0.2506\n",
      "Epoch 900/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - accuracy: 0.2500 - loss: 0.2506\n",
      "Epoch 901/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - accuracy: 0.2500 - loss: 0.2506\n",
      "Epoch 902/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - accuracy: 0.2500 - loss: 0.2506\n",
      "Epoch 903/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - accuracy: 0.2500 - loss: 0.2506\n",
      "Epoch 904/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - accuracy: 0.2500 - loss: 0.2506\n",
      "Epoch 905/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - accuracy: 0.2500 - loss: 0.2506\n",
      "Epoch 906/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - accuracy: 0.2500 - loss: 0.2506\n",
      "Epoch 907/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - accuracy: 0.2500 - loss: 0.2506\n",
      "Epoch 908/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - accuracy: 0.2500 - loss: 0.2506\n",
      "Epoch 909/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - accuracy: 0.2500 - loss: 0.2506\n",
      "Epoch 910/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - accuracy: 0.2500 - loss: 0.2506\n",
      "Epoch 911/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - accuracy: 0.2500 - loss: 0.2506\n",
      "Epoch 912/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - accuracy: 0.2500 - loss: 0.2506\n",
      "Epoch 913/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - accuracy: 0.2500 - loss: 0.2506\n",
      "Epoch 914/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - accuracy: 0.2500 - loss: 0.2506\n",
      "Epoch 915/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - accuracy: 0.2500 - loss: 0.2506\n",
      "Epoch 916/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - accuracy: 0.2500 - loss: 0.2506\n",
      "Epoch 917/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - accuracy: 0.2500 - loss: 0.2506\n",
      "Epoch 918/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 188ms/step - accuracy: 0.2500 - loss: 0.2506\n",
      "Epoch 919/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - accuracy: 0.2500 - loss: 0.2506\n",
      "Epoch 920/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - accuracy: 0.2500 - loss: 0.2506\n",
      "Epoch 921/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - accuracy: 0.2500 - loss: 0.2506\n",
      "Epoch 922/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - accuracy: 0.2500 - loss: 0.2506\n",
      "Epoch 923/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - accuracy: 0.2500 - loss: 0.2506\n",
      "Epoch 924/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - accuracy: 0.2500 - loss: 0.2506\n",
      "Epoch 925/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - accuracy: 0.2500 - loss: 0.2506\n",
      "Epoch 926/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - accuracy: 0.2500 - loss: 0.2506\n",
      "Epoch 927/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - accuracy: 0.2500 - loss: 0.2506\n",
      "Epoch 928/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - accuracy: 0.2500 - loss: 0.2506\n",
      "Epoch 929/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - accuracy: 0.2500 - loss: 0.2506\n",
      "Epoch 930/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - accuracy: 0.2500 - loss: 0.2506\n",
      "Epoch 931/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - accuracy: 0.2500 - loss: 0.2506\n",
      "Epoch 932/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 186ms/step - accuracy: 0.2500 - loss: 0.2506\n",
      "Epoch 933/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - accuracy: 0.2500 - loss: 0.2505\n",
      "Epoch 934/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - accuracy: 0.2500 - loss: 0.2505\n",
      "Epoch 935/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - accuracy: 0.2500 - loss: 0.2505\n",
      "Epoch 936/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - accuracy: 0.2500 - loss: 0.2505\n",
      "Epoch 937/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - accuracy: 0.2500 - loss: 0.2505\n",
      "Epoch 938/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - accuracy: 0.2500 - loss: 0.2505\n",
      "Epoch 939/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - accuracy: 0.2500 - loss: 0.2505\n",
      "Epoch 940/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - accuracy: 0.2500 - loss: 0.2505\n",
      "Epoch 941/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - accuracy: 0.2500 - loss: 0.2505\n",
      "Epoch 942/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - accuracy: 0.2500 - loss: 0.2505\n",
      "Epoch 943/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - accuracy: 0.2500 - loss: 0.2505\n",
      "Epoch 944/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - accuracy: 0.2500 - loss: 0.2505\n",
      "Epoch 945/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 164ms/step - accuracy: 0.2500 - loss: 0.2505\n",
      "Epoch 946/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - accuracy: 0.2500 - loss: 0.2505\n",
      "Epoch 947/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - accuracy: 0.2500 - loss: 0.2505\n",
      "Epoch 948/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - accuracy: 0.2500 - loss: 0.2505\n",
      "Epoch 949/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - accuracy: 0.2500 - loss: 0.2505\n",
      "Epoch 950/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - accuracy: 0.2500 - loss: 0.2505\n",
      "Epoch 951/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - accuracy: 0.2500 - loss: 0.2505\n",
      "Epoch 952/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - accuracy: 0.2500 - loss: 0.2505\n",
      "Epoch 953/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - accuracy: 0.2500 - loss: 0.2505\n",
      "Epoch 954/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - accuracy: 0.2500 - loss: 0.2505\n",
      "Epoch 955/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - accuracy: 0.2500 - loss: 0.2505\n",
      "Epoch 956/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - accuracy: 0.2500 - loss: 0.2505\n",
      "Epoch 957/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - accuracy: 0.2500 - loss: 0.2505\n",
      "Epoch 958/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 167ms/step - accuracy: 0.2500 - loss: 0.2505\n",
      "Epoch 959/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - accuracy: 0.2500 - loss: 0.2505\n",
      "Epoch 960/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - accuracy: 0.2500 - loss: 0.2505\n",
      "Epoch 961/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - accuracy: 0.2500 - loss: 0.2505\n",
      "Epoch 962/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - accuracy: 0.2500 - loss: 0.2505\n",
      "Epoch 963/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - accuracy: 0.2500 - loss: 0.2505\n",
      "Epoch 964/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - accuracy: 0.2500 - loss: 0.2505\n",
      "Epoch 965/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - accuracy: 0.2500 - loss: 0.2505\n",
      "Epoch 966/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - accuracy: 0.2500 - loss: 0.2505\n",
      "Epoch 967/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - accuracy: 0.2500 - loss: 0.2505\n",
      "Epoch 968/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - accuracy: 0.2500 - loss: 0.2505\n",
      "Epoch 969/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - accuracy: 0.2500 - loss: 0.2505\n",
      "Epoch 970/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - accuracy: 0.2500 - loss: 0.2505\n",
      "Epoch 971/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 199ms/step - accuracy: 0.2500 - loss: 0.2505\n",
      "Epoch 972/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - accuracy: 0.2500 - loss: 0.2505\n",
      "Epoch 973/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - accuracy: 0.2500 - loss: 0.2505\n",
      "Epoch 974/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - accuracy: 0.2500 - loss: 0.2505\n",
      "Epoch 975/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - accuracy: 0.2500 - loss: 0.2505\n",
      "Epoch 976/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - accuracy: 0.2500 - loss: 0.2505\n",
      "Epoch 977/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - accuracy: 0.2500 - loss: 0.2505\n",
      "Epoch 978/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - accuracy: 0.2500 - loss: 0.2505\n",
      "Epoch 979/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - accuracy: 0.2500 - loss: 0.2505\n",
      "Epoch 980/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - accuracy: 0.2500 - loss: 0.2505\n",
      "Epoch 981/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - accuracy: 0.2500 - loss: 0.2505\n",
      "Epoch 982/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - accuracy: 0.2500 - loss: 0.2505\n",
      "Epoch 983/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 113ms/step - accuracy: 0.2500 - loss: 0.2505\n",
      "Epoch 984/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85ms/step - accuracy: 0.2500 - loss: 0.2505\n",
      "Epoch 985/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - accuracy: 0.2500 - loss: 0.2505\n",
      "Epoch 986/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - accuracy: 0.2500 - loss: 0.2505\n",
      "Epoch 987/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - accuracy: 0.2500 - loss: 0.2505\n",
      "Epoch 988/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - accuracy: 0.2500 - loss: 0.2505\n",
      "Epoch 989/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - accuracy: 0.2500 - loss: 0.2505\n",
      "Epoch 990/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - accuracy: 0.2500 - loss: 0.2505\n",
      "Epoch 991/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - accuracy: 0.2500 - loss: 0.2505\n",
      "Epoch 992/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - accuracy: 0.2500 - loss: 0.2505\n",
      "Epoch 993/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - accuracy: 0.2500 - loss: 0.2505\n",
      "Epoch 994/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - accuracy: 0.2500 - loss: 0.2505\n",
      "Epoch 995/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - accuracy: 0.2500 - loss: 0.2505\n",
      "Epoch 996/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - accuracy: 0.2500 - loss: 0.2505\n",
      "Epoch 997/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - accuracy: 0.2500 - loss: 0.2505\n",
      "Epoch 998/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - accuracy: 0.2500 - loss: 0.2505\n",
      "Epoch 999/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - accuracy: 0.2500 - loss: 0.2505\n",
      "Epoch 1000/1000\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - accuracy: 0.2500 - loss: 0.2505\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(x, y, epochs=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f6442bb5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,4))\n",
    "plt.subplot(1,2,1)\n",
    "plt.plot(history.history['loss'])\n",
    "plt.title('Loss')\n",
    "plt.subplot(1,2,2)\n",
    "plt.plot(history.history['accuracy'])\n",
    "plt.title('Accuracy')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ce0b9bc9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xx , yy = np.meshgrid(np.linspace(0,1,100),np.linspace(0,1,100))\n",
    "grid = np.c_[xx.ravel(),yy.ravel()]\n",
    "Z = model.predict(grid, verbose=0).reshape(xx.shape)\n",
    "\n",
    "plt.figure(figsize=(8,6))\n",
    "plt.contour(xx,yy,Z,levels=50, cmap = \"RdBu\", alpha=0.8)\n",
    "plt.colorbar(label = \"Prediction\")\n",
    "plt.scatter(x[:,0], x[:,1], c=y.flatten(), cmap=\"RdBu\", edgecolors='k', s=100)\n",
    "plt.xlabel(\"Feature 1\")\n",
    "plt.ylabel(\"Feature 2\")\n",
    "plt.title(\"Decision Boundary XOR\")\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e6b0c0bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,4))\n",
    "plt.subplot(1,2,1)\n",
    "plt.plot(history.history['loss'])\n",
    "plt.title('Loss')\n",
    "plt.subplot(1,2,2)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "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": "3.12.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}