trainer.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "import json\n",
    "import pickle\n",
    "import numpy as np\n",
    "import nltk\n",
    "from nltk.stem import WordNetLemmatizer\n",
    "from keras.models import Sequential, Model\n",
    "from keras.layers import Dense, Activation, Dropout, Embedding, Bidirectional, LSTM, Input, Attention, Flatten, Concatenate  \n",
    "from keras.optimizers import SGD\n",
    "import gensim.models\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "lemmatizer = WordNetLemmatizer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "w2v_model = gensim.models.Word2Vec.load(\"data/output/gram.model\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "intents = json.loads(open('data/input/intents.json').read())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "words = []\n",
    "classes = []\n",
    "documents = []\n",
    "ignore_letters = ['?', '!', '.', ',']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "for intent in intents['intents']:\n",
    "    for pattern in intent['patterns']:\n",
    "        word_list = nltk.word_tokenize(pattern)\n",
    "        words.extend(word_list)\n",
    "        documents.append((word_list, intent['tag']))\n",
    "        if intent['tag'] not in classes:\n",
    "            classes.append(intent['tag'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "words = [lemmatizer.lemmatize(word.lower()) for word in words if word not in ignore_letters]\n",
    "words = sorted(set(words))\n",
    "\n",
    "classes = sorted(set(classes))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle.dump(words, open('data/output/Attention/words.pkl', 'wb'))\n",
    "pickle.dump(classes, open('data/output/Attention/classes.pkl', 'wb'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "training = []\n",
    "output_empty = [0] * len(classes)\n",
    "\n",
    "for document in documents:\n",
    "    bag = []\n",
    "    word_patterns = document[0]\n",
    "    word_patterns = [lemmatizer.lemmatize(word.lower()) for word in word_patterns]\n",
    "    for word in words:\n",
    "        bag.append(1) if word in word_patterns else bag.append(0)\n",
    "\n",
    "    output_row = list(output_empty)\n",
    "    output_row[classes.index(document[1])] = 1\n",
    "    training.append([bag, output_row])\n",
    "\n",
    "random.shuffle(training)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Separate train_x and train_y\n",
    "train_x = np.array([item[0] for item in training])\n",
    "train_y = np.array([item[1] for item in training])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_layer = Input(shape=(len(train_x[0]),))\n",
    "embedding_layer = Embedding(input_dim=len(words), output_dim=100)(input_layer)\n",
    "lstm_layer = Bidirectional(LSTM(130, return_sequences=True))(embedding_layer)\n",
    "attention1 = Attention()([lstm_layer, lstm_layer])\n",
    "attention1 = Flatten()(attention1)\n",
    "lstm_layer2 = Bidirectional(LSTM(130, return_sequences=True))(lstm_layer)\n",
    "attention2 = Attention()([lstm_layer2, lstm_layer2])\n",
    "attention2 = Flatten()(attention2)\n",
    "concatenated_attention = Concatenate()([attention1, attention2])\n",
    "output_layer = Dense(len(train_y[0]), activation='softmax')(concatenated_attention)\n",
    "\n",
    "model = Model(inputs=[input_layer], outputs=[output_layer])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "sgd = SGD(learning_rate=0.01, momentum=0.9, nesterov=True)\n",
    "model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1500\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\ACER\\Desktop\\Mayabati---Your-Personal-AI-Chef\\.venv\\Lib\\site-packages\\keras\\src\\models\\functional.py:237: UserWarning: The structure of `inputs` doesn't match the expected structure.\n",
      "Expected: ['keras_tensor_10']\n",
      "Received: inputs=Tensor(shape=(None, 142))\n",
      "  warnings.warn(msg)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 273ms/step - accuracy: 0.1464 - loss: 2.3779\n",
      "Epoch 2/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 253ms/step - accuracy: 0.1724 - loss: 2.1957\n",
      "Epoch 3/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 250ms/step - accuracy: 0.1777 - loss: 2.2390\n",
      "Epoch 4/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 258ms/step - accuracy: 0.2551 - loss: 2.1353\n",
      "Epoch 5/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 268ms/step - accuracy: 0.1958 - loss: 2.1948\n",
      "Epoch 6/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 266ms/step - accuracy: 0.2255 - loss: 2.1639\n",
      "Epoch 7/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 274ms/step - accuracy: 0.1538 - loss: 2.2041\n",
      "Epoch 8/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 264ms/step - accuracy: 0.1427 - loss: 2.2027\n",
      "Epoch 9/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 238ms/step - accuracy: 0.1398 - loss: 2.1614\n",
      "Epoch 10/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 236ms/step - accuracy: 0.1752 - loss: 2.0909\n",
      "Epoch 11/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 254ms/step - accuracy: 0.1988 - loss: 2.1760\n",
      "Epoch 12/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 261ms/step - accuracy: 0.2051 - loss: 2.1798\n",
      "Epoch 13/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 247ms/step - accuracy: 0.2209 - loss: 2.1586\n",
      "Epoch 14/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 248ms/step - accuracy: 0.1504 - loss: 2.1316\n",
      "Epoch 15/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 253ms/step - accuracy: 0.1588 - loss: 2.1553\n",
      "Epoch 16/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 308ms/step - accuracy: 0.2038 - loss: 2.1559\n",
      "Epoch 17/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 319ms/step - accuracy: 0.2372 - loss: 2.1737\n",
      "Epoch 18/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 270ms/step - accuracy: 0.2166 - loss: 2.1863\n",
      "Epoch 19/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 302ms/step - accuracy: 0.2097 - loss: 2.1013\n",
      "Epoch 20/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 280ms/step - accuracy: 0.2538 - loss: 2.1816\n",
      "Epoch 21/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 257ms/step - accuracy: 0.2001 - loss: 2.1886\n",
      "Epoch 22/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 254ms/step - accuracy: 0.1501 - loss: 2.1547\n",
      "Epoch 23/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 256ms/step - accuracy: 0.2022 - loss: 2.0970\n",
      "Epoch 24/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 257ms/step - accuracy: 0.1656 - loss: 2.2597\n",
      "Epoch 25/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 259ms/step - accuracy: 0.2424 - loss: 2.1390\n",
      "Epoch 26/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 250ms/step - accuracy: 0.2074 - loss: 2.1169\n",
      "Epoch 27/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 244ms/step - accuracy: 0.2555 - loss: 2.1261\n",
      "Epoch 28/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 246ms/step - accuracy: 0.2198 - loss: 2.1705\n",
      "Epoch 29/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 247ms/step - accuracy: 0.2049 - loss: 2.1450\n",
      "Epoch 30/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 243ms/step - accuracy: 0.1515 - loss: 2.1665\n",
      "Epoch 31/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 244ms/step - accuracy: 0.2268 - loss: 2.1124\n",
      "Epoch 32/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 243ms/step - accuracy: 0.2127 - loss: 2.1129\n",
      "Epoch 33/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 245ms/step - accuracy: 0.1758 - loss: 2.2010\n",
      "Epoch 34/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 249ms/step - accuracy: 0.2178 - loss: 2.1660\n",
      "Epoch 35/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.1882 - loss: 2.1243\n",
      "Epoch 36/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 242ms/step - accuracy: 0.1708 - loss: 2.1173\n",
      "Epoch 37/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 246ms/step - accuracy: 0.1708 - loss: 2.2222\n",
      "Epoch 38/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 243ms/step - accuracy: 0.2088 - loss: 2.1525\n",
      "Epoch 39/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 246ms/step - accuracy: 0.1450 - loss: 2.1221\n",
      "Epoch 40/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 245ms/step - accuracy: 0.1960 - loss: 2.1623\n",
      "Epoch 41/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.1950 - loss: 2.1464\n",
      "Epoch 42/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 247ms/step - accuracy: 0.2724 - loss: 2.1371\n",
      "Epoch 43/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 242ms/step - accuracy: 0.1701 - loss: 2.1302\n",
      "Epoch 44/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 244ms/step - accuracy: 0.1847 - loss: 2.1389\n",
      "Epoch 45/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 243ms/step - accuracy: 0.2549 - loss: 2.0741\n",
      "Epoch 46/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 239ms/step - accuracy: 0.2252 - loss: 2.1606\n",
      "Epoch 47/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 238ms/step - accuracy: 0.2519 - loss: 2.1046\n",
      "Epoch 48/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 236ms/step - accuracy: 0.2867 - loss: 2.0706\n",
      "Epoch 49/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 234ms/step - accuracy: 0.2125 - loss: 2.1230\n",
      "Epoch 50/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 236ms/step - accuracy: 0.2470 - loss: 2.1582\n",
      "Epoch 51/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 234ms/step - accuracy: 0.2633 - loss: 2.0996\n",
      "Epoch 52/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 234ms/step - accuracy: 0.2018 - loss: 2.0879\n",
      "Epoch 53/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 232ms/step - accuracy: 0.2216 - loss: 2.1134\n",
      "Epoch 54/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 235ms/step - accuracy: 0.2515 - loss: 2.1199\n",
      "Epoch 55/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 236ms/step - accuracy: 0.2835 - loss: 2.1215\n",
      "Epoch 56/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 238ms/step - accuracy: 0.2787 - loss: 2.0333\n",
      "Epoch 57/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 233ms/step - accuracy: 0.1852 - loss: 2.2933\n",
      "Epoch 58/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 236ms/step - accuracy: 0.1838 - loss: 2.3042\n",
      "Epoch 59/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 235ms/step - accuracy: 0.2290 - loss: 2.0879\n",
      "Epoch 60/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 234ms/step - accuracy: 0.1815 - loss: 2.1284\n",
      "Epoch 61/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 234ms/step - accuracy: 0.1712 - loss: 2.1437\n",
      "Epoch 62/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 235ms/step - accuracy: 0.2035 - loss: 2.1175\n",
      "Epoch 63/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 235ms/step - accuracy: 0.3065 - loss: 2.1022\n",
      "Epoch 64/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 234ms/step - accuracy: 0.2544 - loss: 2.0839\n",
      "Epoch 65/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 236ms/step - accuracy: 0.2582 - loss: 2.0960\n",
      "Epoch 66/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 236ms/step - accuracy: 0.2067 - loss: 2.1042\n",
      "Epoch 67/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 234ms/step - accuracy: 0.2491 - loss: 2.0774\n",
      "Epoch 68/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 237ms/step - accuracy: 0.2853 - loss: 2.1090\n",
      "Epoch 69/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 245ms/step - accuracy: 0.3231 - loss: 1.9497\n",
      "Epoch 70/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 235ms/step - accuracy: 0.2984 - loss: 2.1569\n",
      "Epoch 71/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 235ms/step - accuracy: 0.2153 - loss: 1.9862\n",
      "Epoch 72/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 236ms/step - accuracy: 0.2754 - loss: 1.9367\n",
      "Epoch 73/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 234ms/step - accuracy: 0.3419 - loss: 1.9903\n",
      "Epoch 74/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 233ms/step - accuracy: 0.1741 - loss: 1.9684\n",
      "Epoch 75/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 234ms/step - accuracy: 0.3030 - loss: 2.0210\n",
      "Epoch 76/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 235ms/step - accuracy: 0.3258 - loss: 2.1624\n",
      "Epoch 77/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 238ms/step - accuracy: 0.2432 - loss: 2.2250\n",
      "Epoch 78/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 238ms/step - accuracy: 0.2425 - loss: 2.1260\n",
      "Epoch 79/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 239ms/step - accuracy: 0.3409 - loss: 1.9490\n",
      "Epoch 80/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 232ms/step - accuracy: 0.1998 - loss: 2.0258\n",
      "Epoch 81/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 235ms/step - accuracy: 0.2640 - loss: 2.0407\n",
      "Epoch 82/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 235ms/step - accuracy: 0.1511 - loss: 2.0071\n",
      "Epoch 83/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 236ms/step - accuracy: 0.2723 - loss: 1.9109\n",
      "Epoch 84/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 236ms/step - accuracy: 0.3907 - loss: 1.9410\n",
      "Epoch 85/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.3124 - loss: 1.8108\n",
      "Epoch 86/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 236ms/step - accuracy: 0.4661 - loss: 1.6210\n",
      "Epoch 87/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 238ms/step - accuracy: 0.2521 - loss: 2.5987\n",
      "Epoch 88/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 232ms/step - accuracy: 0.5971 - loss: 1.5887\n",
      "Epoch 89/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 234ms/step - accuracy: 0.6095 - loss: 1.5433\n",
      "Epoch 90/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 233ms/step - accuracy: 0.5887 - loss: 1.4347\n",
      "Epoch 91/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 237ms/step - accuracy: 0.6343 - loss: 1.2032\n",
      "Epoch 92/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 237ms/step - accuracy: 0.5247 - loss: 1.4505\n",
      "Epoch 93/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 232ms/step - accuracy: 0.6342 - loss: 0.9897\n",
      "Epoch 94/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.7014 - loss: 0.9247\n",
      "Epoch 95/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 235ms/step - accuracy: 0.8164 - loss: 0.8346\n",
      "Epoch 96/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 235ms/step - accuracy: 0.7498 - loss: 0.7368\n",
      "Epoch 97/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 233ms/step - accuracy: 0.8588 - loss: 0.5384\n",
      "Epoch 98/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 236ms/step - accuracy: 0.8597 - loss: 0.5416\n",
      "Epoch 99/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 237ms/step - accuracy: 0.8484 - loss: 0.5955\n",
      "Epoch 100/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 224ms/step - accuracy: 0.8652 - loss: 0.3899\n",
      "Epoch 101/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 229ms/step - accuracy: 0.8614 - loss: 0.3557\n",
      "Epoch 102/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 230ms/step - accuracy: 0.9326 - loss: 0.3338\n",
      "Epoch 103/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 247ms/step - accuracy: 0.9387 - loss: 0.2695\n",
      "Epoch 104/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 224ms/step - accuracy: 0.9715 - loss: 0.2297\n",
      "Epoch 105/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 253ms/step - accuracy: 0.9594 - loss: 0.2309\n",
      "Epoch 106/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 235ms/step - accuracy: 0.9168 - loss: 0.2392\n",
      "Epoch 107/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 228ms/step - accuracy: 0.9195 - loss: 0.2361\n",
      "Epoch 108/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 226ms/step - accuracy: 0.9864 - loss: 0.1740\n",
      "Epoch 109/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 226ms/step - accuracy: 0.9635 - loss: 0.1589\n",
      "Epoch 110/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 225ms/step - accuracy: 0.9764 - loss: 0.1446\n",
      "Epoch 111/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 229ms/step - accuracy: 0.9764 - loss: 0.1252\n",
      "Epoch 112/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 226ms/step - accuracy: 0.9811 - loss: 0.1079\n",
      "Epoch 113/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 226ms/step - accuracy: 0.9516 - loss: 0.1214\n",
      "Epoch 114/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 228ms/step - accuracy: 0.9886 - loss: 0.0688\n",
      "Epoch 115/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 231ms/step - accuracy: 0.9725 - loss: 0.1418\n",
      "Epoch 116/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 225ms/step - accuracy: 0.9574 - loss: 0.1233\n",
      "Epoch 117/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 225ms/step - accuracy: 0.9811 - loss: 0.0721\n",
      "Epoch 118/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 225ms/step - accuracy: 0.9877 - loss: 0.0915\n",
      "Epoch 119/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 224ms/step - accuracy: 0.9766 - loss: 0.1070\n",
      "Epoch 120/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 227ms/step - accuracy: 0.9910 - loss: 0.0768\n",
      "Epoch 121/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 225ms/step - accuracy: 0.9824 - loss: 0.0788\n",
      "Epoch 122/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 226ms/step - accuracy: 0.9725 - loss: 0.1254\n",
      "Epoch 123/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 224ms/step - accuracy: 0.9875 - loss: 0.0694\n",
      "Epoch 124/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 233ms/step - accuracy: 0.9925 - loss: 0.0586\n",
      "Epoch 125/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 224ms/step - accuracy: 0.9849 - loss: 0.0833\n",
      "Epoch 126/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 226ms/step - accuracy: 0.9855 - loss: 0.0735\n",
      "Epoch 127/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 228ms/step - accuracy: 0.9712 - loss: 0.0847\n",
      "Epoch 128/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 227ms/step - accuracy: 0.9740 - loss: 0.0673\n",
      "Epoch 129/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 223ms/step - accuracy: 0.9747 - loss: 0.0755\n",
      "Epoch 130/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 226ms/step - accuracy: 0.9910 - loss: 0.0532\n",
      "Epoch 131/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 226ms/step - accuracy: 0.9877 - loss: 0.0578\n",
      "Epoch 132/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 232ms/step - accuracy: 0.9790 - loss: 0.0678\n",
      "Epoch 133/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 227ms/step - accuracy: 0.9901 - loss: 0.0466\n",
      "Epoch 134/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 228ms/step - accuracy: 0.9476 - loss: 0.0816\n",
      "Epoch 135/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 227ms/step - accuracy: 0.9938 - loss: 0.0464\n",
      "Epoch 136/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 229ms/step - accuracy: 0.9849 - loss: 0.0643\n",
      "Epoch 137/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 227ms/step - accuracy: 0.9589 - loss: 0.0821\n",
      "Epoch 138/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 224ms/step - accuracy: 0.9864 - loss: 0.0506\n",
      "Epoch 139/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 226ms/step - accuracy: 0.9737 - loss: 0.0637\n",
      "Epoch 140/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 228ms/step - accuracy: 0.9737 - loss: 0.0588\n",
      "Epoch 141/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 229ms/step - accuracy: 0.9890 - loss: 0.0348\n",
      "Epoch 142/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 229ms/step - accuracy: 0.9857 - loss: 0.0448\n",
      "Epoch 143/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 228ms/step - accuracy: 0.9550 - loss: 0.0576\n",
      "Epoch 144/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 229ms/step - accuracy: 0.9737 - loss: 0.0533\n",
      "Epoch 145/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 225ms/step - accuracy: 0.9609 - loss: 0.0644\n",
      "Epoch 146/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 219ms/step - accuracy: 0.9837 - loss: 0.0379\n",
      "Epoch 147/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 218ms/step - accuracy: 0.9890 - loss: 0.0396\n",
      "Epoch 148/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 223ms/step - accuracy: 0.9675 - loss: 0.0524\n",
      "Epoch 149/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 221ms/step - accuracy: 0.9825 - loss: 0.0582\n",
      "Epoch 150/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 218ms/step - accuracy: 0.9622 - loss: 0.0522\n",
      "Epoch 151/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 208ms/step - accuracy: 0.9838 - loss: 0.0543\n",
      "Epoch 152/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 229ms/step - accuracy: 0.9925 - loss: 0.0544\n",
      "Epoch 153/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 220ms/step - accuracy: 0.9938 - loss: 0.0370\n",
      "Epoch 154/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 218ms/step - accuracy: 0.9890 - loss: 0.0675\n",
      "Epoch 155/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 219ms/step - accuracy: 0.9766 - loss: 0.0794\n",
      "Epoch 156/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 219ms/step - accuracy: 0.9766 - loss: 0.0578\n",
      "Epoch 157/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 218ms/step - accuracy: 0.9875 - loss: 0.0463\n",
      "Epoch 158/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 228ms/step - accuracy: 0.9694 - loss: 0.0531\n",
      "Epoch 159/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 225ms/step - accuracy: 0.9802 - loss: 0.0437\n",
      "Epoch 160/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 225ms/step - accuracy: 0.9903 - loss: 0.0433\n",
      "Epoch 161/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 228ms/step - accuracy: 0.9890 - loss: 0.0479\n",
      "Epoch 162/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 228ms/step - accuracy: 0.9894 - loss: 0.0309\n",
      "Epoch 163/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 231ms/step - accuracy: 0.9470 - loss: 0.0801\n",
      "Epoch 164/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 229ms/step - accuracy: 0.9776 - loss: 0.0456\n",
      "Epoch 165/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 227ms/step - accuracy: 0.9910 - loss: 0.0324\n",
      "Epoch 166/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 229ms/step - accuracy: 0.9864 - loss: 0.0534\n",
      "Epoch 167/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 226ms/step - accuracy: 0.9613 - loss: 0.0588\n",
      "Epoch 168/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 228ms/step - accuracy: 0.9727 - loss: 0.0607\n",
      "Epoch 169/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 230ms/step - accuracy: 0.9925 - loss: 0.0359\n",
      "Epoch 170/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 226ms/step - accuracy: 0.9760 - loss: 0.0410\n",
      "Epoch 171/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 242ms/step - accuracy: 0.9875 - loss: 0.0423\n",
      "Epoch 172/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9725 - loss: 0.0563\n",
      "Epoch 173/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 228ms/step - accuracy: 0.9864 - loss: 0.0407\n",
      "Epoch 174/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 227ms/step - accuracy: 0.9910 - loss: 0.0307\n",
      "Epoch 175/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 227ms/step - accuracy: 0.9938 - loss: 0.0317\n",
      "Epoch 176/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 226ms/step - accuracy: 0.9825 - loss: 0.0429\n",
      "Epoch 177/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 232ms/step - accuracy: 0.9875 - loss: 0.0409\n",
      "Epoch 178/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 226ms/step - accuracy: 0.9829 - loss: 0.0490\n",
      "Epoch 179/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 226ms/step - accuracy: 0.9692 - loss: 0.0529\n",
      "Epoch 180/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 227ms/step - accuracy: 0.9711 - loss: 0.0457\n",
      "Epoch 181/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 227ms/step - accuracy: 0.9890 - loss: 0.0358\n",
      "Epoch 182/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 225ms/step - accuracy: 0.9694 - loss: 0.0464\n",
      "Epoch 183/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 230ms/step - accuracy: 0.9692 - loss: 0.0458\n",
      "Epoch 184/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 231ms/step - accuracy: 0.9910 - loss: 0.0387\n",
      "Epoch 185/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 227ms/step - accuracy: 0.9862 - loss: 0.0556\n",
      "Epoch 186/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 231ms/step - accuracy: 0.9810 - loss: 0.0591\n",
      "Epoch 187/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 229ms/step - accuracy: 0.9903 - loss: 0.0276\n",
      "Epoch 188/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 227ms/step - accuracy: 0.9877 - loss: 0.0398\n",
      "Epoch 189/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 228ms/step - accuracy: 0.9544 - loss: 0.0684\n",
      "Epoch 190/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 225ms/step - accuracy: 0.9772 - loss: 0.0792\n",
      "Epoch 191/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 229ms/step - accuracy: 0.9725 - loss: 0.0885\n",
      "Epoch 192/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 227ms/step - accuracy: 0.9678 - loss: 0.0810\n",
      "Epoch 193/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 227ms/step - accuracy: 0.9803 - loss: 0.0374\n",
      "Epoch 194/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 225ms/step - accuracy: 0.9864 - loss: 0.0507\n",
      "Epoch 195/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 227ms/step - accuracy: 0.9772 - loss: 0.0513\n",
      "Epoch 196/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 224ms/step - accuracy: 0.9707 - loss: 0.0506\n",
      "Epoch 197/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 225ms/step - accuracy: 0.9890 - loss: 0.0428\n",
      "Epoch 198/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 230ms/step - accuracy: 0.9792 - loss: 0.0451\n",
      "Epoch 199/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 224ms/step - accuracy: 0.9890 - loss: 0.0291\n",
      "Epoch 200/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 225ms/step - accuracy: 0.9910 - loss: 0.0273\n",
      "Epoch 201/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 236ms/step - accuracy: 0.9633 - loss: 0.0564\n",
      "Epoch 202/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 239ms/step - accuracy: 0.9710 - loss: 0.0417\n",
      "Epoch 203/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 227ms/step - accuracy: 0.9753 - loss: 0.0333\n",
      "Epoch 204/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 229ms/step - accuracy: 0.9674 - loss: 0.0450\n",
      "Epoch 205/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 228ms/step - accuracy: 0.9890 - loss: 0.0358\n",
      "Epoch 206/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 227ms/step - accuracy: 0.9810 - loss: 0.0452\n",
      "Epoch 207/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 224ms/step - accuracy: 0.9647 - loss: 0.0647\n",
      "Epoch 208/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 226ms/step - accuracy: 0.9394 - loss: 0.0693\n",
      "Epoch 209/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 226ms/step - accuracy: 0.9938 - loss: 0.0322\n",
      "Epoch 210/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 228ms/step - accuracy: 0.9877 - loss: 0.0443\n",
      "Epoch 211/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 226ms/step - accuracy: 0.9731 - loss: 0.0438\n",
      "Epoch 212/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 228ms/step - accuracy: 0.9764 - loss: 0.0780\n",
      "Epoch 213/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 227ms/step - accuracy: 0.9647 - loss: 0.0776\n",
      "Epoch 214/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 227ms/step - accuracy: 0.9886 - loss: 0.0260\n",
      "Epoch 215/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 226ms/step - accuracy: 0.9877 - loss: 0.0346\n",
      "Epoch 216/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 225ms/step - accuracy: 0.9810 - loss: 0.0532\n",
      "Epoch 217/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 233ms/step - accuracy: 0.9790 - loss: 0.0615\n",
      "Epoch 218/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 226ms/step - accuracy: 0.9825 - loss: 0.0450\n",
      "Epoch 219/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 227ms/step - accuracy: 0.9803 - loss: 0.0299\n",
      "Epoch 220/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 227ms/step - accuracy: 0.9790 - loss: 0.0507\n",
      "Epoch 221/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 223ms/step - accuracy: 0.9785 - loss: 0.0290\n",
      "Epoch 222/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 227ms/step - accuracy: 0.9849 - loss: 0.0607\n",
      "Epoch 223/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 229ms/step - accuracy: 0.9825 - loss: 0.0531\n",
      "Epoch 224/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 244ms/step - accuracy: 0.9825 - loss: 0.0364\n",
      "Epoch 225/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 203ms/step - accuracy: 0.9837 - loss: 0.0275\n",
      "Epoch 226/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 191ms/step - accuracy: 0.9894 - loss: 0.0314\n",
      "Epoch 227/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 192ms/step - accuracy: 0.9609 - loss: 0.0537\n",
      "Epoch 228/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 193ms/step - accuracy: 0.9923 - loss: 0.0251\n",
      "Epoch 229/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 193ms/step - accuracy: 0.9811 - loss: 0.0314\n",
      "Epoch 230/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 192ms/step - accuracy: 0.9894 - loss: 0.0452\n",
      "Epoch 231/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 191ms/step - accuracy: 0.9894 - loss: 0.0296\n",
      "Epoch 232/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 187ms/step - accuracy: 0.9712 - loss: 0.0545\n",
      "Epoch 233/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 189ms/step - accuracy: 0.9825 - loss: 0.0419\n",
      "Epoch 234/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 189ms/step - accuracy: 0.9890 - loss: 0.0288\n",
      "Epoch 235/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 190ms/step - accuracy: 0.9875 - loss: 0.0424\n",
      "Epoch 236/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 193ms/step - accuracy: 0.9829 - loss: 0.0414\n",
      "Epoch 237/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 189ms/step - accuracy: 0.9757 - loss: 0.0459\n",
      "Epoch 238/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 189ms/step - accuracy: 0.9923 - loss: 0.0261\n",
      "Epoch 239/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 189ms/step - accuracy: 0.9877 - loss: 0.0356\n",
      "Epoch 240/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 188ms/step - accuracy: 0.9938 - loss: 0.0336\n",
      "Epoch 241/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 189ms/step - accuracy: 0.9803 - loss: 0.0357\n",
      "Epoch 242/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 189ms/step - accuracy: 0.9875 - loss: 0.0395\n",
      "Epoch 243/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 188ms/step - accuracy: 0.9790 - loss: 0.0370\n",
      "Epoch 244/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 189ms/step - accuracy: 0.9890 - loss: 0.0445\n",
      "Epoch 245/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 187ms/step - accuracy: 0.9829 - loss: 0.0382\n",
      "Epoch 246/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 189ms/step - accuracy: 0.9647 - loss: 0.0712\n",
      "Epoch 247/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 206ms/step - accuracy: 0.9672 - loss: 0.0471\n",
      "Epoch 248/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 204ms/step - accuracy: 0.9849 - loss: 0.0329\n",
      "Epoch 249/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 202ms/step - accuracy: 0.9613 - loss: 0.0505\n",
      "Epoch 250/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 215ms/step - accuracy: 0.9613 - loss: 0.0504\n",
      "Epoch 251/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 278ms/step - accuracy: 0.9938 - loss: 0.0171\n",
      "Epoch 252/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 234ms/step - accuracy: 0.9875 - loss: 0.0322\n",
      "Epoch 253/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 239ms/step - accuracy: 0.9810 - loss: 0.0472\n",
      "Epoch 254/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 239ms/step - accuracy: 0.9753 - loss: 0.0428\n",
      "Epoch 255/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 238ms/step - accuracy: 0.9910 - loss: 0.0365\n",
      "Epoch 256/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 238ms/step - accuracy: 0.9712 - loss: 0.0514\n",
      "Epoch 257/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 239ms/step - accuracy: 0.9875 - loss: 0.0260\n",
      "Epoch 258/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 279ms/step - accuracy: 0.9864 - loss: 0.0418\n",
      "Epoch 259/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 276ms/step - accuracy: 0.9864 - loss: 0.0281\n",
      "Epoch 260/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 261ms/step - accuracy: 0.9776 - loss: 0.0294\n",
      "Epoch 261/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 250ms/step - accuracy: 0.9727 - loss: 0.0352\n",
      "Epoch 262/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 253ms/step - accuracy: 0.9864 - loss: 0.0259\n",
      "Epoch 263/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 250ms/step - accuracy: 0.9753 - loss: 0.0285\n",
      "Epoch 264/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 251ms/step - accuracy: 0.9495 - loss: 0.0511\n",
      "Epoch 265/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 253ms/step - accuracy: 0.9688 - loss: 0.0506\n",
      "Epoch 266/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 252ms/step - accuracy: 0.9864 - loss: 0.0401\n",
      "Epoch 267/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 257ms/step - accuracy: 0.9790 - loss: 0.0395\n",
      "Epoch 268/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 269ms/step - accuracy: 0.9659 - loss: 0.0500\n",
      "Epoch 269/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 261ms/step - accuracy: 0.9923 - loss: 0.0217\n",
      "Epoch 270/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 254ms/step - accuracy: 0.9790 - loss: 0.0604\n",
      "Epoch 271/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 249ms/step - accuracy: 0.9877 - loss: 0.0404\n",
      "Epoch 272/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 244ms/step - accuracy: 0.9849 - loss: 0.0503\n",
      "Epoch 273/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9903 - loss: 0.0253\n",
      "Epoch 274/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 235ms/step - accuracy: 0.9829 - loss: 0.0744\n",
      "Epoch 275/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 242ms/step - accuracy: 0.9838 - loss: 0.0485\n",
      "Epoch 276/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9764 - loss: 0.0404\n",
      "Epoch 277/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9760 - loss: 0.0681\n",
      "Epoch 278/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 236ms/step - accuracy: 0.9849 - loss: 0.0430\n",
      "Epoch 279/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 235ms/step - accuracy: 0.9849 - loss: 0.0448\n",
      "Epoch 280/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 242ms/step - accuracy: 0.9810 - loss: 0.0403\n",
      "Epoch 281/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9825 - loss: 0.0406\n",
      "Epoch 282/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 242ms/step - accuracy: 0.9496 - loss: 0.0580\n",
      "Epoch 283/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 238ms/step - accuracy: 0.9864 - loss: 0.0327\n",
      "Epoch 284/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 243ms/step - accuracy: 0.9824 - loss: 0.0242\n",
      "Epoch 285/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 248ms/step - accuracy: 0.9731 - loss: 0.0441\n",
      "Epoch 286/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 234ms/step - accuracy: 0.9810 - loss: 0.0438\n",
      "Epoch 287/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 238ms/step - accuracy: 0.9622 - loss: 0.0579\n",
      "Epoch 288/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 254ms/step - accuracy: 0.9747 - loss: 0.0527\n",
      "Epoch 289/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 258ms/step - accuracy: 0.9688 - loss: 0.0558\n",
      "Epoch 290/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 246ms/step - accuracy: 0.9894 - loss: 0.0266\n",
      "Epoch 291/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 244ms/step - accuracy: 0.9890 - loss: 0.0241\n",
      "Epoch 292/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 243ms/step - accuracy: 0.9829 - loss: 0.0415\n",
      "Epoch 293/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 239ms/step - accuracy: 0.9890 - loss: 0.0264\n",
      "Epoch 294/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 243ms/step - accuracy: 0.9910 - loss: 0.0360\n",
      "Epoch 295/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 239ms/step - accuracy: 0.9790 - loss: 0.0394\n",
      "Epoch 296/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 242ms/step - accuracy: 0.9925 - loss: 0.0281\n",
      "Epoch 297/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 238ms/step - accuracy: 0.9903 - loss: 0.0274\n",
      "Epoch 298/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 242ms/step - accuracy: 0.9877 - loss: 0.0342\n",
      "Epoch 299/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 243ms/step - accuracy: 0.9747 - loss: 0.0378\n",
      "Epoch 300/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9903 - loss: 0.0250\n",
      "Epoch 301/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 244ms/step - accuracy: 0.9569 - loss: 0.0612\n",
      "Epoch 302/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9838 - loss: 0.0640\n",
      "Epoch 303/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 239ms/step - accuracy: 0.9829 - loss: 0.0384\n",
      "Epoch 304/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9609 - loss: 0.0623\n",
      "Epoch 305/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 243ms/step - accuracy: 0.9757 - loss: 0.0367\n",
      "Epoch 306/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 243ms/step - accuracy: 0.9877 - loss: 0.0318\n",
      "Epoch 307/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 237ms/step - accuracy: 0.9792 - loss: 0.0305\n",
      "Epoch 308/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 242ms/step - accuracy: 0.9792 - loss: 0.0270\n",
      "Epoch 309/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9534 - loss: 0.0526\n",
      "Epoch 310/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 237ms/step - accuracy: 0.9694 - loss: 0.0482\n",
      "Epoch 311/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9766 - loss: 0.0294\n",
      "Epoch 312/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 239ms/step - accuracy: 0.9626 - loss: 0.0427\n",
      "Epoch 313/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 239ms/step - accuracy: 0.9541 - loss: 0.0534\n",
      "Epoch 314/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9829 - loss: 0.0418\n",
      "Epoch 315/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9910 - loss: 0.0295\n",
      "Epoch 316/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9877 - loss: 0.0232\n",
      "Epoch 317/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9747 - loss: 0.0397\n",
      "Epoch 318/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9753 - loss: 0.0364\n",
      "Epoch 319/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 239ms/step - accuracy: 0.9849 - loss: 0.0310\n",
      "Epoch 320/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 239ms/step - accuracy: 0.9764 - loss: 0.0387\n",
      "Epoch 321/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9818 - loss: 0.0379\n",
      "Epoch 322/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 243ms/step - accuracy: 0.9923 - loss: 0.0322\n",
      "Epoch 323/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 239ms/step - accuracy: 0.9712 - loss: 0.0411\n",
      "Epoch 324/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9686 - loss: 0.0701\n",
      "Epoch 325/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 239ms/step - accuracy: 0.9827 - loss: 0.0276\n",
      "Epoch 326/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9894 - loss: 0.0232\n",
      "Epoch 327/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 247ms/step - accuracy: 0.9903 - loss: 0.0182\n",
      "Epoch 328/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 244ms/step - accuracy: 0.9810 - loss: 0.0474\n",
      "Epoch 329/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 239ms/step - accuracy: 0.9790 - loss: 0.0420\n",
      "Epoch 330/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9910 - loss: 0.0280\n",
      "Epoch 331/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9649 - loss: 0.0449\n",
      "Epoch 332/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 244ms/step - accuracy: 0.9723 - loss: 0.0275\n",
      "Epoch 333/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 248ms/step - accuracy: 0.9838 - loss: 0.0397\n",
      "Epoch 334/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 249ms/step - accuracy: 0.9686 - loss: 0.0472\n",
      "Epoch 335/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9712 - loss: 0.0518\n",
      "Epoch 336/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 237ms/step - accuracy: 0.9810 - loss: 0.0386\n",
      "Epoch 337/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 256ms/step - accuracy: 0.9524 - loss: 0.0463\n",
      "Epoch 338/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 255ms/step - accuracy: 0.9923 - loss: 0.0424\n",
      "Epoch 339/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 257ms/step - accuracy: 0.9855 - loss: 0.0516\n",
      "Epoch 340/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9857 - loss: 0.0380\n",
      "Epoch 341/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 243ms/step - accuracy: 0.9707 - loss: 0.0382\n",
      "Epoch 342/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9712 - loss: 0.0474\n",
      "Epoch 343/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 243ms/step - accuracy: 0.9544 - loss: 0.0969\n",
      "Epoch 344/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9810 - loss: 0.0378\n",
      "Epoch 345/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 239ms/step - accuracy: 0.9443 - loss: 0.0672\n",
      "Epoch 346/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 244ms/step - accuracy: 0.9609 - loss: 0.0466\n",
      "Epoch 347/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 239ms/step - accuracy: 0.9633 - loss: 0.0487\n",
      "Epoch 348/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9635 - loss: 0.0363\n",
      "Epoch 349/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9853 - loss: 0.0257\n",
      "Epoch 350/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9875 - loss: 0.0380\n",
      "Epoch 351/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 245ms/step - accuracy: 0.9649 - loss: 0.0455\n",
      "Epoch 352/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 237ms/step - accuracy: 0.9810 - loss: 0.0434\n",
      "Epoch 353/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9772 - loss: 0.0305\n",
      "Epoch 354/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9589 - loss: 0.0415\n",
      "Epoch 355/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 242ms/step - accuracy: 0.9688 - loss: 0.0353\n",
      "Epoch 356/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 245ms/step - accuracy: 0.9849 - loss: 0.0339\n",
      "Epoch 357/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 242ms/step - accuracy: 0.9686 - loss: 0.0699\n",
      "Epoch 358/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 244ms/step - accuracy: 0.9737 - loss: 0.0364\n",
      "Epoch 359/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 239ms/step - accuracy: 0.9776 - loss: 0.0329\n",
      "Epoch 360/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9850 - loss: 0.0278\n",
      "Epoch 361/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 239ms/step - accuracy: 0.9613 - loss: 0.0516\n",
      "Epoch 362/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9790 - loss: 0.0413\n",
      "Epoch 363/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9633 - loss: 0.0433\n",
      "Epoch 364/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 236ms/step - accuracy: 0.9951 - loss: 0.0123\n",
      "Epoch 365/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 244ms/step - accuracy: 0.9647 - loss: 0.0398\n",
      "Epoch 366/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 238ms/step - accuracy: 0.9622 - loss: 0.0484\n",
      "Epoch 367/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 242ms/step - accuracy: 0.9829 - loss: 0.0382\n",
      "Epoch 368/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9925 - loss: 0.0225\n",
      "Epoch 369/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 242ms/step - accuracy: 0.9613 - loss: 0.0534\n",
      "Epoch 370/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 242ms/step - accuracy: 0.9801 - loss: 0.0352\n",
      "Epoch 371/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 237ms/step - accuracy: 0.9747 - loss: 0.0504\n",
      "Epoch 372/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 236ms/step - accuracy: 0.9923 - loss: 0.0277\n",
      "Epoch 373/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 238ms/step - accuracy: 0.9925 - loss: 0.0253\n",
      "Epoch 374/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9718 - loss: 0.0399\n",
      "Epoch 375/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 239ms/step - accuracy: 0.9633 - loss: 0.0395\n",
      "Epoch 376/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 248ms/step - accuracy: 0.9594 - loss: 0.0531\n",
      "Epoch 377/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 242ms/step - accuracy: 0.9723 - loss: 0.0406\n",
      "Epoch 378/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 237ms/step - accuracy: 0.9938 - loss: 0.0235\n",
      "Epoch 379/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9976 - loss: 0.0207\n",
      "Epoch 380/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 239ms/step - accuracy: 0.9903 - loss: 0.0392\n",
      "Epoch 381/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9570 - loss: 0.0739\n",
      "Epoch 382/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 242ms/step - accuracy: 0.9764 - loss: 0.0505\n",
      "Epoch 383/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 239ms/step - accuracy: 0.9810 - loss: 0.0408\n",
      "Epoch 384/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 244ms/step - accuracy: 0.9864 - loss: 0.0469\n",
      "Epoch 385/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 237ms/step - accuracy: 0.9850 - loss: 0.0212\n",
      "Epoch 386/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 238ms/step - accuracy: 0.9910 - loss: 0.0355\n",
      "Epoch 387/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 239ms/step - accuracy: 0.9747 - loss: 0.0526\n",
      "Epoch 388/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 244ms/step - accuracy: 0.9790 - loss: 0.0418\n",
      "Epoch 389/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9910 - loss: 0.0240\n",
      "Epoch 390/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 243ms/step - accuracy: 0.9692 - loss: 0.0368\n",
      "Epoch 391/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 244ms/step - accuracy: 0.9825 - loss: 0.0305\n",
      "Epoch 392/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 239ms/step - accuracy: 0.9925 - loss: 0.0263\n",
      "Epoch 393/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 242ms/step - accuracy: 0.9649 - loss: 0.0418\n",
      "Epoch 394/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9394 - loss: 0.0708\n",
      "Epoch 395/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9688 - loss: 0.0493\n",
      "Epoch 396/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9864 - loss: 0.0266\n",
      "Epoch 397/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 253ms/step - accuracy: 0.9622 - loss: 0.0334\n",
      "Epoch 398/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 239ms/step - accuracy: 0.9877 - loss: 0.0278\n",
      "Epoch 399/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 243ms/step - accuracy: 0.9938 - loss: 0.0261\n",
      "Epoch 400/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 238ms/step - accuracy: 0.9686 - loss: 0.0494\n",
      "Epoch 401/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 245ms/step - accuracy: 0.9686 - loss: 0.0865\n",
      "Epoch 402/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9829 - loss: 0.0332\n",
      "Epoch 403/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 242ms/step - accuracy: 0.9731 - loss: 0.0472\n",
      "Epoch 404/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9857 - loss: 0.0204\n",
      "Epoch 405/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 239ms/step - accuracy: 0.9739 - loss: 0.0258\n",
      "Epoch 406/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9772 - loss: 0.0247\n",
      "Epoch 407/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9938 - loss: 0.0124\n",
      "Epoch 408/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 239ms/step - accuracy: 0.9825 - loss: 0.0441\n",
      "Epoch 409/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9675 - loss: 0.0399\n",
      "Epoch 410/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 243ms/step - accuracy: 0.9711 - loss: 0.0421\n",
      "Epoch 411/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9662 - loss: 0.0404\n",
      "Epoch 412/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 242ms/step - accuracy: 0.9569 - loss: 0.0587\n",
      "Epoch 413/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 243ms/step - accuracy: 0.9875 - loss: 0.0417\n",
      "Epoch 414/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 246ms/step - accuracy: 0.9594 - loss: 0.0437\n",
      "Epoch 415/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9829 - loss: 0.0519\n",
      "Epoch 416/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 238ms/step - accuracy: 0.9446 - loss: 0.0603\n",
      "Epoch 417/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 239ms/step - accuracy: 0.9747 - loss: 0.0454\n",
      "Epoch 418/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9951 - loss: 0.0165\n",
      "Epoch 419/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 238ms/step - accuracy: 0.9790 - loss: 0.0526\n",
      "Epoch 420/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 242ms/step - accuracy: 0.9747 - loss: 0.0836\n",
      "Epoch 421/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 239ms/step - accuracy: 0.9732 - loss: 0.0377\n",
      "Epoch 422/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 239ms/step - accuracy: 0.9764 - loss: 0.0372\n",
      "Epoch 423/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 238ms/step - accuracy: 0.9890 - loss: 0.0348\n",
      "Epoch 424/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 239ms/step - accuracy: 0.9903 - loss: 0.0294\n",
      "Epoch 425/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9838 - loss: 0.0287\n",
      "Epoch 426/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 238ms/step - accuracy: 0.9803 - loss: 0.0621\n",
      "Epoch 427/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 248ms/step - accuracy: 0.9764 - loss: 0.0442\n",
      "Epoch 428/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 237ms/step - accuracy: 0.9764 - loss: 0.0420\n",
      "Epoch 429/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 239ms/step - accuracy: 0.9694 - loss: 0.0483\n",
      "Epoch 430/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9938 - loss: 0.0302\n",
      "Epoch 431/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 242ms/step - accuracy: 0.9951 - loss: 0.0252\n",
      "Epoch 432/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 238ms/step - accuracy: 0.9875 - loss: 0.0295\n",
      "Epoch 433/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 239ms/step - accuracy: 0.9810 - loss: 0.0582\n",
      "Epoch 434/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 239ms/step - accuracy: 0.9776 - loss: 0.0296\n",
      "Epoch 435/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 245ms/step - accuracy: 0.9829 - loss: 0.0316\n",
      "Epoch 436/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 239ms/step - accuracy: 0.9890 - loss: 0.0333\n",
      "Epoch 437/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 276ms/step - accuracy: 0.9723 - loss: 0.0309\n",
      "Epoch 438/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9875 - loss: 0.0420\n",
      "Epoch 439/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9711 - loss: 0.0513\n",
      "Epoch 440/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 236ms/step - accuracy: 0.9857 - loss: 0.0216\n",
      "Epoch 441/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9686 - loss: 0.0517\n",
      "Epoch 442/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 238ms/step - accuracy: 0.9723 - loss: 0.0360\n",
      "Epoch 443/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 242ms/step - accuracy: 0.9784 - loss: 0.0490\n",
      "Epoch 444/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 237ms/step - accuracy: 0.9890 - loss: 0.0385\n",
      "Epoch 445/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 237ms/step - accuracy: 0.9678 - loss: 0.0653\n",
      "Epoch 446/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9753 - loss: 0.0330\n",
      "Epoch 447/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 238ms/step - accuracy: 0.9837 - loss: 0.0282\n",
      "Epoch 448/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9779 - loss: 0.0279\n",
      "Epoch 449/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 238ms/step - accuracy: 0.9790 - loss: 0.0419\n",
      "Epoch 450/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 244ms/step - accuracy: 0.9938 - loss: 0.0196\n",
      "Epoch 451/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9764 - loss: 0.0354\n",
      "Epoch 452/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 239ms/step - accuracy: 0.9712 - loss: 0.0711\n",
      "Epoch 453/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9760 - loss: 0.0380\n",
      "Epoch 454/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 239ms/step - accuracy: 0.9686 - loss: 0.0432\n",
      "Epoch 455/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 242ms/step - accuracy: 0.9875 - loss: 0.0309\n",
      "Epoch 456/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 248ms/step - accuracy: 0.9825 - loss: 0.0284\n",
      "Epoch 457/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 252ms/step - accuracy: 0.9810 - loss: 0.0387\n",
      "Epoch 458/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 250ms/step - accuracy: 0.9894 - loss: 0.0324\n",
      "Epoch 459/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 253ms/step - accuracy: 0.9633 - loss: 0.0452\n",
      "Epoch 460/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 246ms/step - accuracy: 0.9818 - loss: 0.0263\n",
      "Epoch 461/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 244ms/step - accuracy: 0.9697 - loss: 0.0300\n",
      "Epoch 462/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 250ms/step - accuracy: 0.9875 - loss: 0.0320\n",
      "Epoch 463/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 257ms/step - accuracy: 0.9827 - loss: 0.0344\n",
      "Epoch 464/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 242ms/step - accuracy: 0.9903 - loss: 0.0377\n",
      "Epoch 465/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 239ms/step - accuracy: 0.9725 - loss: 0.0340\n",
      "Epoch 466/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 243ms/step - accuracy: 0.9910 - loss: 0.0269\n",
      "Epoch 467/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 243ms/step - accuracy: 0.9862 - loss: 0.0436\n",
      "Epoch 468/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9810 - loss: 0.0392\n",
      "Epoch 469/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9788 - loss: 0.0247\n",
      "Epoch 470/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 239ms/step - accuracy: 0.9894 - loss: 0.0247\n",
      "Epoch 471/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 242ms/step - accuracy: 0.9792 - loss: 0.0327\n",
      "Epoch 472/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 239ms/step - accuracy: 0.9766 - loss: 0.0386\n",
      "Epoch 473/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9757 - loss: 0.0275\n",
      "Epoch 474/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9624 - loss: 0.0368\n",
      "Epoch 475/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 242ms/step - accuracy: 0.9747 - loss: 0.0420\n",
      "Epoch 476/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 239ms/step - accuracy: 0.9790 - loss: 0.0473\n",
      "Epoch 477/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 239ms/step - accuracy: 0.9903 - loss: 0.0232\n",
      "Epoch 478/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9864 - loss: 0.0297\n",
      "Epoch 479/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 239ms/step - accuracy: 0.9829 - loss: 0.0368\n",
      "Epoch 480/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 238ms/step - accuracy: 0.9910 - loss: 0.0216\n",
      "Epoch 481/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9825 - loss: 0.0342\n",
      "Epoch 482/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 245ms/step - accuracy: 0.9864 - loss: 0.0374\n",
      "Epoch 483/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 239ms/step - accuracy: 0.9694 - loss: 0.0358\n",
      "Epoch 484/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9766 - loss: 0.0328\n",
      "Epoch 485/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 238ms/step - accuracy: 0.9829 - loss: 0.0384\n",
      "Epoch 486/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 244ms/step - accuracy: 0.9574 - loss: 0.0504\n",
      "Epoch 487/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 238ms/step - accuracy: 0.9910 - loss: 0.0257\n",
      "Epoch 488/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9700 - loss: 0.0295\n",
      "Epoch 489/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 239ms/step - accuracy: 0.9785 - loss: 0.0289\n",
      "Epoch 490/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9864 - loss: 0.0369\n",
      "Epoch 491/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9776 - loss: 0.0260\n",
      "Epoch 492/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9760 - loss: 0.0446\n",
      "Epoch 493/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9923 - loss: 0.0296\n",
      "Epoch 494/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 244ms/step - accuracy: 0.9710 - loss: 0.0575\n",
      "Epoch 495/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9516 - loss: 0.0892\n",
      "Epoch 496/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 238ms/step - accuracy: 0.9772 - loss: 0.0422\n",
      "Epoch 497/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 239ms/step - accuracy: 0.9764 - loss: 0.0444\n",
      "Epoch 498/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 242ms/step - accuracy: 0.9750 - loss: 0.0313\n",
      "Epoch 499/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 238ms/step - accuracy: 0.9879 - loss: 0.0207\n",
      "Epoch 500/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 242ms/step - accuracy: 0.9910 - loss: 0.0486\n",
      "Epoch 501/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9760 - loss: 0.0843\n",
      "Epoch 502/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 239ms/step - accuracy: 0.9902 - loss: 0.0365\n",
      "Epoch 503/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 239ms/step - accuracy: 0.9731 - loss: 0.0943\n",
      "Epoch 504/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 239ms/step - accuracy: 0.9894 - loss: 0.0337\n",
      "Epoch 505/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 243ms/step - accuracy: 0.9801 - loss: 0.0417\n",
      "Epoch 506/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 238ms/step - accuracy: 0.9837 - loss: 0.0195\n",
      "Epoch 507/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 236ms/step - accuracy: 0.9825 - loss: 0.0364\n",
      "Epoch 508/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 237ms/step - accuracy: 0.9622 - loss: 0.0470\n",
      "Epoch 509/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 242ms/step - accuracy: 0.9757 - loss: 0.0296\n",
      "Epoch 510/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 238ms/step - accuracy: 0.9747 - loss: 0.0473\n",
      "Epoch 511/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9574 - loss: 0.0562\n",
      "Epoch 512/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 245ms/step - accuracy: 0.9622 - loss: 0.0371\n",
      "Epoch 513/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 242ms/step - accuracy: 0.9796 - loss: 0.0232\n",
      "Epoch 514/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 237ms/step - accuracy: 0.9827 - loss: 0.0209\n",
      "Epoch 515/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 237ms/step - accuracy: 0.9837 - loss: 0.0198\n",
      "Epoch 516/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 244ms/step - accuracy: 0.9628 - loss: 0.0352\n",
      "Epoch 517/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 239ms/step - accuracy: 0.9849 - loss: 0.0271\n",
      "Epoch 518/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9790 - loss: 0.0445\n",
      "Epoch 519/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 242ms/step - accuracy: 0.9576 - loss: 0.0471\n",
      "Epoch 520/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9639 - loss: 0.0336\n",
      "Epoch 521/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 238ms/step - accuracy: 0.9855 - loss: 0.0372\n",
      "Epoch 522/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 237ms/step - accuracy: 0.9850 - loss: 0.0218\n",
      "Epoch 523/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 244ms/step - accuracy: 0.9825 - loss: 0.0402\n",
      "Epoch 524/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 243ms/step - accuracy: 0.9864 - loss: 0.0349\n",
      "Epoch 525/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9910 - loss: 0.0187\n",
      "Epoch 526/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 243ms/step - accuracy: 0.9712 - loss: 0.0436\n",
      "Epoch 527/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 243ms/step - accuracy: 0.9855 - loss: 0.0332\n",
      "Epoch 528/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 243ms/step - accuracy: 0.9502 - loss: 0.0417\n",
      "Epoch 529/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9864 - loss: 0.0230\n",
      "Epoch 530/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 239ms/step - accuracy: 0.9574 - loss: 0.0538\n",
      "Epoch 531/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9796 - loss: 0.0315\n",
      "Epoch 532/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 243ms/step - accuracy: 0.9938 - loss: 0.0246\n",
      "Epoch 533/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 264ms/step - accuracy: 0.9938 - loss: 0.0214\n",
      "Epoch 534/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 251ms/step - accuracy: 0.9750 - loss: 0.0328\n",
      "Epoch 535/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 250ms/step - accuracy: 0.9925 - loss: 0.0202\n",
      "Epoch 536/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 248ms/step - accuracy: 0.9766 - loss: 0.0282\n",
      "Epoch 537/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 242ms/step - accuracy: 0.9864 - loss: 0.0275\n",
      "Epoch 538/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9647 - loss: 0.0608\n",
      "Epoch 539/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 237ms/step - accuracy: 0.9711 - loss: 0.0399\n",
      "Epoch 540/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 244ms/step - accuracy: 0.9731 - loss: 0.0470\n",
      "Epoch 541/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 238ms/step - accuracy: 0.9731 - loss: 0.0471\n",
      "Epoch 542/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 242ms/step - accuracy: 0.9718 - loss: 0.0360\n",
      "Epoch 543/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 242ms/step - accuracy: 0.9639 - loss: 0.0317\n",
      "Epoch 544/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 242ms/step - accuracy: 0.9802 - loss: 0.0236\n",
      "Epoch 545/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9925 - loss: 0.0187\n",
      "Epoch 546/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 237ms/step - accuracy: 0.9903 - loss: 0.0221\n",
      "Epoch 547/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9910 - loss: 0.0379\n",
      "Epoch 548/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9837 - loss: 0.0230\n",
      "Epoch 549/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9431 - loss: 0.0612\n",
      "Epoch 550/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 243ms/step - accuracy: 0.9622 - loss: 0.0464\n",
      "Epoch 551/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 242ms/step - accuracy: 0.9877 - loss: 0.0243\n",
      "Epoch 552/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 243ms/step - accuracy: 0.9894 - loss: 0.0219\n",
      "Epoch 553/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 239ms/step - accuracy: 0.9886 - loss: 0.0186\n",
      "Epoch 554/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 234ms/step - accuracy: 0.9712 - loss: 0.0425\n",
      "Epoch 555/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9678 - loss: 0.0499\n",
      "Epoch 556/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9633 - loss: 0.0450\n",
      "Epoch 557/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9548 - loss: 0.0477\n",
      "Epoch 558/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 243ms/step - accuracy: 0.9704 - loss: 0.0258\n",
      "Epoch 559/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 242ms/step - accuracy: 0.9639 - loss: 0.0409\n",
      "Epoch 560/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9923 - loss: 0.0183\n",
      "Epoch 561/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 244ms/step - accuracy: 0.9850 - loss: 0.0255\n",
      "Epoch 562/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9890 - loss: 0.0288\n",
      "Epoch 563/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 237ms/step - accuracy: 0.9938 - loss: 0.0203\n",
      "Epoch 564/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 238ms/step - accuracy: 0.9712 - loss: 0.0442\n",
      "Epoch 565/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 239ms/step - accuracy: 0.9531 - loss: 0.0870\n",
      "Epoch 566/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 243ms/step - accuracy: 0.9877 - loss: 0.0329\n",
      "Epoch 567/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 238ms/step - accuracy: 0.9864 - loss: 0.0265\n",
      "Epoch 568/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 239ms/step - accuracy: 0.9855 - loss: 0.0371\n",
      "Epoch 569/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9801 - loss: 0.0277\n",
      "Epoch 570/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 242ms/step - accuracy: 0.9727 - loss: 0.0388\n",
      "Epoch 571/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 244ms/step - accuracy: 0.9796 - loss: 0.0294\n",
      "Epoch 572/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 243ms/step - accuracy: 0.9649 - loss: 0.0612\n",
      "Epoch 573/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 236ms/step - accuracy: 0.9910 - loss: 0.0241\n",
      "Epoch 574/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9609 - loss: 0.0472\n",
      "Epoch 575/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 246ms/step - accuracy: 0.9647 - loss: 0.0573\n",
      "Epoch 576/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 239ms/step - accuracy: 0.9829 - loss: 0.0358\n",
      "Epoch 577/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 242ms/step - accuracy: 0.9890 - loss: 0.0260\n",
      "Epoch 578/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9803 - loss: 0.0444\n",
      "Epoch 579/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 242ms/step - accuracy: 0.9925 - loss: 0.0243\n",
      "Epoch 580/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 245ms/step - accuracy: 0.9890 - loss: 0.0254\n",
      "Epoch 581/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 247ms/step - accuracy: 0.9790 - loss: 0.0537\n",
      "Epoch 582/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 243ms/step - accuracy: 0.9827 - loss: 0.0283\n",
      "Epoch 583/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 246ms/step - accuracy: 0.9694 - loss: 0.0367\n",
      "Epoch 584/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 239ms/step - accuracy: 0.9456 - loss: 0.0519\n",
      "Epoch 585/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9849 - loss: 0.0351\n",
      "Epoch 586/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 244ms/step - accuracy: 0.9694 - loss: 0.0274\n",
      "Epoch 587/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9894 - loss: 0.0305\n",
      "Epoch 588/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 245ms/step - accuracy: 0.9837 - loss: 0.0225\n",
      "Epoch 589/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9803 - loss: 0.0492\n",
      "Epoch 590/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9727 - loss: 0.0405\n",
      "Epoch 591/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 244ms/step - accuracy: 0.9737 - loss: 0.0391\n",
      "Epoch 592/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 237ms/step - accuracy: 0.9694 - loss: 0.0322\n",
      "Epoch 593/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9633 - loss: 0.0420\n",
      "Epoch 594/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 247ms/step - accuracy: 0.9639 - loss: 0.0363\n",
      "Epoch 595/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 249ms/step - accuracy: 0.9333 - loss: 0.0560\n",
      "Epoch 596/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 248ms/step - accuracy: 0.9890 - loss: 0.0206\n",
      "Epoch 597/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 239ms/step - accuracy: 0.9764 - loss: 0.0442\n",
      "Epoch 598/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 239ms/step - accuracy: 0.9541 - loss: 0.0489\n",
      "Epoch 599/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 245ms/step - accuracy: 0.9659 - loss: 0.0413\n",
      "Epoch 600/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9938 - loss: 0.0248\n",
      "Epoch 601/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 248ms/step - accuracy: 0.9747 - loss: 0.0280\n",
      "Epoch 602/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9925 - loss: 0.0247\n",
      "Epoch 603/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 238ms/step - accuracy: 0.9925 - loss: 0.0238\n",
      "Epoch 604/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 248ms/step - accuracy: 0.9875 - loss: 0.0301\n",
      "Epoch 605/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 249ms/step - accuracy: 0.9877 - loss: 0.0249\n",
      "Epoch 606/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 247ms/step - accuracy: 0.9925 - loss: 0.0239\n",
      "Epoch 607/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9574 - loss: 0.0702\n",
      "Epoch 608/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 239ms/step - accuracy: 0.9855 - loss: 0.0350\n",
      "Epoch 609/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 244ms/step - accuracy: 0.9700 - loss: 0.0270\n",
      "Epoch 610/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 239ms/step - accuracy: 0.9509 - loss: 0.0540\n",
      "Epoch 611/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 243ms/step - accuracy: 0.9857 - loss: 0.0302\n",
      "Epoch 612/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 242ms/step - accuracy: 0.9694 - loss: 0.0374\n",
      "Epoch 613/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9837 - loss: 0.0289\n",
      "Epoch 614/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 243ms/step - accuracy: 0.9731 - loss: 0.0403\n",
      "Epoch 615/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 243ms/step - accuracy: 0.9910 - loss: 0.0224\n",
      "Epoch 616/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9776 - loss: 0.0327\n",
      "Epoch 617/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 244ms/step - accuracy: 0.9570 - loss: 0.0450\n",
      "Epoch 618/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 239ms/step - accuracy: 0.9938 - loss: 0.0329\n",
      "Epoch 619/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9659 - loss: 0.0538\n",
      "Epoch 620/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 245ms/step - accuracy: 0.9764 - loss: 0.0477\n",
      "Epoch 621/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 236ms/step - accuracy: 0.9864 - loss: 0.0255\n",
      "Epoch 622/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 248ms/step - accuracy: 0.9877 - loss: 0.0268\n",
      "Epoch 623/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 242ms/step - accuracy: 0.9548 - loss: 0.0443\n",
      "Epoch 624/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 239ms/step - accuracy: 0.9838 - loss: 0.0405\n",
      "Epoch 625/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 242ms/step - accuracy: 0.9829 - loss: 0.0362\n",
      "Epoch 626/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 242ms/step - accuracy: 0.9731 - loss: 0.0457\n",
      "Epoch 627/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 242ms/step - accuracy: 0.9853 - loss: 0.0281\n",
      "Epoch 628/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 238ms/step - accuracy: 0.9875 - loss: 0.0279\n",
      "Epoch 629/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 245ms/step - accuracy: 0.9877 - loss: 0.0361\n",
      "Epoch 630/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 244ms/step - accuracy: 0.9838 - loss: 0.0280\n",
      "Epoch 631/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9810 - loss: 0.0431\n",
      "Epoch 632/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 257ms/step - accuracy: 0.9790 - loss: 0.0441\n",
      "Epoch 633/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 252ms/step - accuracy: 0.9901 - loss: 0.0176\n",
      "Epoch 634/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 239ms/step - accuracy: 0.9903 - loss: 0.0222\n",
      "Epoch 635/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 239ms/step - accuracy: 0.9737 - loss: 0.0361\n",
      "Epoch 636/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 239ms/step - accuracy: 0.9810 - loss: 0.0482\n",
      "Epoch 637/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9649 - loss: 0.0410\n",
      "Epoch 638/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9694 - loss: 0.0418\n",
      "Epoch 639/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 236ms/step - accuracy: 0.9864 - loss: 0.0348\n",
      "Epoch 640/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 238ms/step - accuracy: 0.9727 - loss: 0.0392\n",
      "Epoch 641/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 238ms/step - accuracy: 0.9910 - loss: 0.0263\n",
      "Epoch 642/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 237ms/step - accuracy: 0.9803 - loss: 0.0290\n",
      "Epoch 643/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9712 - loss: 0.0452\n",
      "Epoch 644/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 238ms/step - accuracy: 0.9890 - loss: 0.0276\n",
      "Epoch 645/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 245ms/step - accuracy: 0.9776 - loss: 0.0225\n",
      "Epoch 646/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9825 - loss: 0.0361\n",
      "Epoch 647/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 248ms/step - accuracy: 0.9840 - loss: 0.0313\n",
      "Epoch 648/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 237ms/step - accuracy: 0.9875 - loss: 0.0247\n",
      "Epoch 649/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 245ms/step - accuracy: 0.9711 - loss: 0.0378\n",
      "Epoch 650/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9659 - loss: 0.0369\n",
      "Epoch 651/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9747 - loss: 0.0379\n",
      "Epoch 652/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 246ms/step - accuracy: 0.9772 - loss: 0.0287\n",
      "Epoch 653/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 239ms/step - accuracy: 0.9766 - loss: 0.0251\n",
      "Epoch 654/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9925 - loss: 0.0194\n",
      "Epoch 655/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 243ms/step - accuracy: 0.9886 - loss: 0.0149\n",
      "Epoch 656/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9902 - loss: 0.0416\n",
      "Epoch 657/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 239ms/step - accuracy: 0.9736 - loss: 0.0439\n",
      "Epoch 658/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9872 - loss: 0.0165\n",
      "Epoch 659/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9925 - loss: 0.0187\n",
      "Epoch 660/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 244ms/step - accuracy: 0.9633 - loss: 0.0450\n",
      "Epoch 661/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 242ms/step - accuracy: 0.9910 - loss: 0.0251\n",
      "Epoch 662/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 239ms/step - accuracy: 0.9511 - loss: 0.0528\n",
      "Epoch 663/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 249ms/step - accuracy: 0.9772 - loss: 0.0273\n",
      "Epoch 664/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9647 - loss: 0.0515\n",
      "Epoch 665/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 242ms/step - accuracy: 0.9890 - loss: 0.0236\n",
      "Epoch 666/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9910 - loss: 0.0201\n",
      "Epoch 667/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 239ms/step - accuracy: 0.9829 - loss: 0.0341\n",
      "Epoch 668/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 250ms/step - accuracy: 0.9886 - loss: 0.0133\n",
      "Epoch 669/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 237ms/step - accuracy: 0.9790 - loss: 0.0330\n",
      "Epoch 670/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 230ms/step - accuracy: 0.9923 - loss: 0.0183\n",
      "Epoch 671/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 238ms/step - accuracy: 0.9862 - loss: 0.0386\n",
      "Epoch 672/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 239ms/step - accuracy: 0.9772 - loss: 0.0316\n",
      "Epoch 673/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9757 - loss: 0.0303\n",
      "Epoch 674/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 238ms/step - accuracy: 0.9790 - loss: 0.0373\n",
      "Epoch 675/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 238ms/step - accuracy: 0.9764 - loss: 0.0455\n",
      "Epoch 676/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 249ms/step - accuracy: 0.9747 - loss: 0.0388\n",
      "Epoch 677/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9747 - loss: 0.0514\n",
      "Epoch 678/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9840 - loss: 0.0170\n",
      "Epoch 679/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 246ms/step - accuracy: 0.9838 - loss: 0.0411\n",
      "Epoch 680/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 239ms/step - accuracy: 0.9509 - loss: 0.0542\n",
      "Epoch 681/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9764 - loss: 0.0524\n",
      "Epoch 682/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 242ms/step - accuracy: 0.9609 - loss: 0.0514\n",
      "Epoch 683/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 244ms/step - accuracy: 0.9692 - loss: 0.0337\n",
      "Epoch 684/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 248ms/step - accuracy: 0.9849 - loss: 0.0402\n",
      "Epoch 685/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 242ms/step - accuracy: 0.9870 - loss: 0.0227\n",
      "Epoch 686/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 242ms/step - accuracy: 0.9910 - loss: 0.0200\n",
      "Epoch 687/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 245ms/step - accuracy: 0.9894 - loss: 0.0198\n",
      "Epoch 688/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9657 - loss: 0.0361\n",
      "Epoch 689/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9788 - loss: 0.0226\n",
      "Epoch 690/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 239ms/step - accuracy: 0.9710 - loss: 0.0364\n",
      "Epoch 691/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9674 - loss: 0.0303\n",
      "Epoch 692/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 246ms/step - accuracy: 0.9810 - loss: 0.0405\n",
      "Epoch 693/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 242ms/step - accuracy: 0.9712 - loss: 0.0422\n",
      "Epoch 694/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9639 - loss: 0.0407\n",
      "Epoch 695/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 242ms/step - accuracy: 0.9818 - loss: 0.0352\n",
      "Epoch 696/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 243ms/step - accuracy: 0.9890 - loss: 0.0296\n",
      "Epoch 697/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 242ms/step - accuracy: 0.9910 - loss: 0.0321\n",
      "Epoch 698/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 246ms/step - accuracy: 0.9613 - loss: 0.0400\n",
      "Epoch 699/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 242ms/step - accuracy: 0.9875 - loss: 0.0263\n",
      "Epoch 700/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 247ms/step - accuracy: 0.9587 - loss: 0.0395\n",
      "Epoch 701/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9855 - loss: 0.0305\n",
      "Epoch 702/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 243ms/step - accuracy: 0.9692 - loss: 0.0419\n",
      "Epoch 703/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 247ms/step - accuracy: 0.9560 - loss: 0.0429\n",
      "Epoch 704/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9723 - loss: 0.0330\n",
      "Epoch 705/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 244ms/step - accuracy: 0.9647 - loss: 0.0515\n",
      "Epoch 706/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 238ms/step - accuracy: 0.9686 - loss: 0.0617\n",
      "Epoch 707/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 243ms/step - accuracy: 0.9766 - loss: 0.0270\n",
      "Epoch 708/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 244ms/step - accuracy: 0.9810 - loss: 0.0303\n",
      "Epoch 709/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 239ms/step - accuracy: 0.9829 - loss: 0.0331\n",
      "Epoch 710/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 242ms/step - accuracy: 0.9910 - loss: 0.0203\n",
      "Epoch 711/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9678 - loss: 0.0395\n",
      "Epoch 712/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 244ms/step - accuracy: 0.9803 - loss: 0.0363\n",
      "Epoch 713/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 246ms/step - accuracy: 0.9890 - loss: 0.0280\n",
      "Epoch 714/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 242ms/step - accuracy: 0.9855 - loss: 0.0303\n",
      "Epoch 715/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9826 - loss: 0.0197\n",
      "Epoch 716/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 247ms/step - accuracy: 0.9803 - loss: 0.0465\n",
      "Epoch 717/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9686 - loss: 0.0595\n",
      "Epoch 718/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 243ms/step - accuracy: 0.9875 - loss: 0.0344\n",
      "Epoch 719/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9737 - loss: 0.0311\n",
      "Epoch 720/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 245ms/step - accuracy: 0.9825 - loss: 0.0261\n",
      "Epoch 721/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9776 - loss: 0.0303\n",
      "Epoch 722/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 242ms/step - accuracy: 0.9810 - loss: 0.0381\n",
      "Epoch 723/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9524 - loss: 0.0421\n",
      "Epoch 724/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 237ms/step - accuracy: 0.9866 - loss: 0.0213\n",
      "Epoch 725/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 242ms/step - accuracy: 0.9731 - loss: 0.0544\n",
      "Epoch 726/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 239ms/step - accuracy: 0.9910 - loss: 0.0208\n",
      "Epoch 727/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 248ms/step - accuracy: 0.9694 - loss: 0.0496\n",
      "Epoch 728/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9925 - loss: 0.0164\n",
      "Epoch 729/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 243ms/step - accuracy: 0.9890 - loss: 0.0274\n",
      "Epoch 730/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 243ms/step - accuracy: 0.9890 - loss: 0.0239\n",
      "Epoch 731/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 245ms/step - accuracy: 0.9594 - loss: 0.0407\n",
      "Epoch 732/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9731 - loss: 0.0416\n",
      "Epoch 733/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9511 - loss: 0.0437\n",
      "Epoch 734/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 253ms/step - accuracy: 0.9938 - loss: 0.0234\n",
      "Epoch 735/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 242ms/step - accuracy: 0.9672 - loss: 0.0534\n",
      "Epoch 736/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 244ms/step - accuracy: 0.9849 - loss: 0.0320\n",
      "Epoch 737/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 238ms/step - accuracy: 0.9923 - loss: 0.0260\n",
      "Epoch 738/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 244ms/step - accuracy: 0.9963 - loss: 0.0223\n",
      "Epoch 739/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 245ms/step - accuracy: 0.9875 - loss: 0.0281\n",
      "Epoch 740/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 245ms/step - accuracy: 0.9923 - loss: 0.0196\n",
      "Epoch 741/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 239ms/step - accuracy: 0.9875 - loss: 0.0290\n",
      "Epoch 742/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 243ms/step - accuracy: 0.9649 - loss: 0.0459\n",
      "Epoch 743/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 245ms/step - accuracy: 0.9731 - loss: 0.0335\n",
      "Epoch 744/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 239ms/step - accuracy: 0.9910 - loss: 0.0154\n",
      "Epoch 745/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9866 - loss: 0.0266\n",
      "Epoch 746/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 251ms/step - accuracy: 0.9875 - loss: 0.0246\n",
      "Epoch 747/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 248ms/step - accuracy: 0.9622 - loss: 0.0466\n",
      "Epoch 748/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 238ms/step - accuracy: 0.9864 - loss: 0.0240\n",
      "Epoch 749/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 244ms/step - accuracy: 0.9694 - loss: 0.0369\n",
      "Epoch 750/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 246ms/step - accuracy: 0.9609 - loss: 0.0486\n",
      "Epoch 751/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 239ms/step - accuracy: 0.9294 - loss: 0.0664\n",
      "Epoch 752/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 246ms/step - accuracy: 0.9609 - loss: 0.0432\n",
      "Epoch 753/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9910 - loss: 0.0272\n",
      "Epoch 754/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9731 - loss: 0.0399\n",
      "Epoch 755/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9788 - loss: 0.0345\n",
      "Epoch 756/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 246ms/step - accuracy: 0.9757 - loss: 0.0268\n",
      "Epoch 757/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 242ms/step - accuracy: 0.9747 - loss: 0.0371\n",
      "Epoch 758/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 245ms/step - accuracy: 0.9864 - loss: 0.0279\n",
      "Epoch 759/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 239ms/step - accuracy: 0.9760 - loss: 0.0396\n",
      "Epoch 760/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 236ms/step - accuracy: 0.9810 - loss: 0.0348\n",
      "Epoch 761/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 239ms/step - accuracy: 0.9731 - loss: 0.0487\n",
      "Epoch 762/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 238ms/step - accuracy: 0.9776 - loss: 0.0342\n",
      "Epoch 763/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 244ms/step - accuracy: 0.9864 - loss: 0.0343\n",
      "Epoch 764/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 256ms/step - accuracy: 0.9750 - loss: 0.0335\n",
      "Epoch 765/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 252ms/step - accuracy: 0.9541 - loss: 0.0419\n",
      "Epoch 766/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 229ms/step - accuracy: 0.9788 - loss: 0.0225\n",
      "Epoch 767/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 259ms/step - accuracy: 0.9675 - loss: 0.0402\n",
      "Epoch 768/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 256ms/step - accuracy: 0.9938 - loss: 0.0344\n",
      "Epoch 769/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9855 - loss: 0.0260\n",
      "Epoch 770/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 250ms/step - accuracy: 0.9838 - loss: 0.0270\n",
      "Epoch 771/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 239ms/step - accuracy: 0.9910 - loss: 0.0241\n",
      "Epoch 772/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 239ms/step - accuracy: 0.9923 - loss: 0.0187\n",
      "Epoch 773/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 243ms/step - accuracy: 0.9910 - loss: 0.0213\n",
      "Epoch 774/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 244ms/step - accuracy: 0.9894 - loss: 0.0240\n",
      "Epoch 775/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 243ms/step - accuracy: 0.9725 - loss: 0.0395\n",
      "Epoch 776/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 246ms/step - accuracy: 0.9849 - loss: 0.0290\n",
      "Epoch 777/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 243ms/step - accuracy: 0.9737 - loss: 0.0401\n",
      "Epoch 778/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 245ms/step - accuracy: 0.9805 - loss: 0.0240\n",
      "Epoch 779/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 243ms/step - accuracy: 0.9675 - loss: 0.0368\n",
      "Epoch 780/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 244ms/step - accuracy: 0.9649 - loss: 0.0527\n",
      "Epoch 781/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9802 - loss: 0.0236\n",
      "Epoch 782/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 242ms/step - accuracy: 0.9495 - loss: 0.0454\n",
      "Epoch 783/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 239ms/step - accuracy: 0.9864 - loss: 0.0367\n",
      "Epoch 784/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 242ms/step - accuracy: 0.9766 - loss: 0.0271\n",
      "Epoch 785/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 248ms/step - accuracy: 0.9712 - loss: 0.0433\n",
      "Epoch 786/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 256ms/step - accuracy: 0.9779 - loss: 0.0359\n",
      "Epoch 787/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 256ms/step - accuracy: 0.9925 - loss: 0.0316\n",
      "Epoch 788/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 239ms/step - accuracy: 0.9890 - loss: 0.0229\n",
      "Epoch 789/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 243ms/step - accuracy: 0.9910 - loss: 0.0255\n",
      "Epoch 790/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 245ms/step - accuracy: 0.9875 - loss: 0.0247\n",
      "Epoch 791/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 242ms/step - accuracy: 0.9837 - loss: 0.0213\n",
      "Epoch 792/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9596 - loss: 0.0337\n",
      "Epoch 793/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9509 - loss: 0.0556\n",
      "Epoch 794/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 248ms/step - accuracy: 0.9938 - loss: 0.0126\n",
      "Epoch 795/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 239ms/step - accuracy: 0.9890 - loss: 0.0366\n",
      "Epoch 796/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 239ms/step - accuracy: 0.9910 - loss: 0.0267\n",
      "Epoch 797/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9766 - loss: 0.0338\n",
      "Epoch 798/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9825 - loss: 0.0369\n",
      "Epoch 799/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 246ms/step - accuracy: 0.9712 - loss: 0.0542\n",
      "Epoch 800/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 257ms/step - accuracy: 0.9849 - loss: 0.0324\n",
      "Epoch 801/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 247ms/step - accuracy: 0.9829 - loss: 0.0309\n",
      "Epoch 802/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 256ms/step - accuracy: 0.9796 - loss: 0.0332\n",
      "Epoch 803/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 244ms/step - accuracy: 0.9723 - loss: 0.0307\n",
      "Epoch 804/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 245ms/step - accuracy: 0.9875 - loss: 0.0305\n",
      "Epoch 805/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 242ms/step - accuracy: 0.9725 - loss: 0.0502\n",
      "Epoch 806/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 242ms/step - accuracy: 0.9810 - loss: 0.0365\n",
      "Epoch 807/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 246ms/step - accuracy: 0.9910 - loss: 0.0355\n",
      "Epoch 808/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 243ms/step - accuracy: 0.9659 - loss: 0.0470\n",
      "Epoch 809/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 248ms/step - accuracy: 0.9785 - loss: 0.0319\n",
      "Epoch 810/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 243ms/step - accuracy: 0.9925 - loss: 0.0280\n",
      "Epoch 811/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9764 - loss: 0.0396\n",
      "Epoch 812/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 244ms/step - accuracy: 0.9675 - loss: 0.0488\n",
      "Epoch 813/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 248ms/step - accuracy: 0.9855 - loss: 0.0219\n",
      "Epoch 814/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 246ms/step - accuracy: 0.9723 - loss: 0.0356\n",
      "Epoch 815/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9890 - loss: 0.0246\n",
      "Epoch 816/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 248ms/step - accuracy: 0.9849 - loss: 0.0307\n",
      "Epoch 817/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9712 - loss: 0.0552\n",
      "Epoch 818/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 239ms/step - accuracy: 0.9443 - loss: 0.0598\n",
      "Epoch 819/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 243ms/step - accuracy: 0.9750 - loss: 0.0339\n",
      "Epoch 820/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 238ms/step - accuracy: 0.9790 - loss: 0.0366\n",
      "Epoch 821/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 246ms/step - accuracy: 0.9711 - loss: 0.0406\n",
      "Epoch 822/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 238ms/step - accuracy: 0.9910 - loss: 0.0304\n",
      "Epoch 823/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 245ms/step - accuracy: 0.9537 - loss: 0.0440\n",
      "Epoch 824/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 239ms/step - accuracy: 0.9875 - loss: 0.0275\n",
      "Epoch 825/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 247ms/step - accuracy: 0.9731 - loss: 0.0473\n",
      "Epoch 826/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9875 - loss: 0.0253\n",
      "Epoch 827/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9753 - loss: 0.0358\n",
      "Epoch 828/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 239ms/step - accuracy: 0.9910 - loss: 0.0201\n",
      "Epoch 829/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 246ms/step - accuracy: 0.9472 - loss: 0.0503\n",
      "Epoch 830/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 242ms/step - accuracy: 0.9827 - loss: 0.0204\n",
      "Epoch 831/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 237ms/step - accuracy: 0.9596 - loss: 0.0338\n",
      "Epoch 832/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9701 - loss: 0.0438\n",
      "Epoch 833/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9675 - loss: 0.0321\n",
      "Epoch 834/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 244ms/step - accuracy: 0.9647 - loss: 0.0611\n",
      "Epoch 835/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 239ms/step - accuracy: 0.9910 - loss: 0.0254\n",
      "Epoch 836/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 238ms/step - accuracy: 0.9544 - loss: 0.0517\n",
      "Epoch 837/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 245ms/step - accuracy: 0.9903 - loss: 0.0242\n",
      "Epoch 838/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 243ms/step - accuracy: 0.9723 - loss: 0.0388\n",
      "Epoch 839/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9587 - loss: 0.0476\n",
      "Epoch 840/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 245ms/step - accuracy: 0.9633 - loss: 0.0500\n",
      "Epoch 841/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 242ms/step - accuracy: 0.9785 - loss: 0.0252\n",
      "Epoch 842/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 245ms/step - accuracy: 0.9725 - loss: 0.0389\n",
      "Epoch 843/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 239ms/step - accuracy: 0.9659 - loss: 0.0453\n",
      "Epoch 844/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 247ms/step - accuracy: 0.9740 - loss: 0.0451\n",
      "Epoch 845/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9731 - loss: 0.0454\n",
      "Epoch 846/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 242ms/step - accuracy: 0.9810 - loss: 0.0373\n",
      "Epoch 847/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 245ms/step - accuracy: 0.9736 - loss: 0.0342\n",
      "Epoch 848/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 243ms/step - accuracy: 0.9849 - loss: 0.0336\n",
      "Epoch 849/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 242ms/step - accuracy: 0.9810 - loss: 0.0407\n",
      "Epoch 850/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 245ms/step - accuracy: 0.9654 - loss: 0.0290\n",
      "Epoch 851/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 244ms/step - accuracy: 0.9903 - loss: 0.0204\n",
      "Epoch 852/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 244ms/step - accuracy: 0.9864 - loss: 0.0232\n",
      "Epoch 853/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 248ms/step - accuracy: 0.9903 - loss: 0.0196\n",
      "Epoch 854/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 244ms/step - accuracy: 0.9747 - loss: 0.0382\n",
      "Epoch 855/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 244ms/step - accuracy: 0.9764 - loss: 0.0426\n",
      "Epoch 856/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9829 - loss: 0.0428\n",
      "Epoch 857/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9615 - loss: 0.0393\n",
      "Epoch 858/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9548 - loss: 0.0444\n",
      "Epoch 859/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9531 - loss: 0.0697\n",
      "Epoch 860/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9864 - loss: 0.0239\n",
      "Epoch 861/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 242ms/step - accuracy: 0.9737 - loss: 0.0378\n",
      "Epoch 862/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9675 - loss: 0.0350\n",
      "Epoch 863/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 237ms/step - accuracy: 0.9772 - loss: 0.0357\n",
      "Epoch 864/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9923 - loss: 0.0268\n",
      "Epoch 865/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9910 - loss: 0.0291\n",
      "Epoch 866/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9853 - loss: 0.0225\n",
      "Epoch 867/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 239ms/step - accuracy: 0.9433 - loss: 0.0518\n",
      "Epoch 868/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 236ms/step - accuracy: 0.9890 - loss: 0.0289\n",
      "Epoch 869/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9613 - loss: 0.0442\n",
      "Epoch 870/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 238ms/step - accuracy: 0.9718 - loss: 0.0355\n",
      "Epoch 871/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 246ms/step - accuracy: 0.9810 - loss: 0.0426\n",
      "Epoch 872/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 236ms/step - accuracy: 0.9792 - loss: 0.0256\n",
      "Epoch 873/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 243ms/step - accuracy: 0.9910 - loss: 0.0214\n",
      "Epoch 874/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 237ms/step - accuracy: 0.9731 - loss: 0.0392\n",
      "Epoch 875/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9659 - loss: 0.0406\n",
      "Epoch 876/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9649 - loss: 0.0353\n",
      "Epoch 877/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 243ms/step - accuracy: 0.9894 - loss: 0.0206\n",
      "Epoch 878/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9894 - loss: 0.0297\n",
      "Epoch 879/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 248ms/step - accuracy: 0.9938 - loss: 0.0126\n",
      "Epoch 880/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9910 - loss: 0.0204\n",
      "Epoch 881/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 246ms/step - accuracy: 0.9925 - loss: 0.0177\n",
      "Epoch 882/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 238ms/step - accuracy: 0.9853 - loss: 0.0191\n",
      "Epoch 883/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 246ms/step - accuracy: 0.9875 - loss: 0.0198\n",
      "Epoch 884/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9894 - loss: 0.0264\n",
      "Epoch 885/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 245ms/step - accuracy: 0.9938 - loss: 0.0248\n",
      "Epoch 886/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 237ms/step - accuracy: 0.9902 - loss: 0.0320\n",
      "Epoch 887/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 242ms/step - accuracy: 0.9531 - loss: 0.0767\n",
      "Epoch 888/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 244ms/step - accuracy: 0.9875 - loss: 0.0270\n",
      "Epoch 889/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 242ms/step - accuracy: 0.9811 - loss: 0.0293\n",
      "Epoch 890/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 248ms/step - accuracy: 0.9890 - loss: 0.0250\n",
      "Epoch 891/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 243ms/step - accuracy: 0.9875 - loss: 0.0195\n",
      "Epoch 892/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 249ms/step - accuracy: 0.9877 - loss: 0.0318\n",
      "Epoch 893/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9855 - loss: 0.0280\n",
      "Epoch 894/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 247ms/step - accuracy: 0.9718 - loss: 0.0326\n",
      "Epoch 895/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 245ms/step - accuracy: 0.9753 - loss: 0.0313\n",
      "Epoch 896/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 247ms/step - accuracy: 0.9757 - loss: 0.0323\n",
      "Epoch 897/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 243ms/step - accuracy: 0.9524 - loss: 0.0388\n",
      "Epoch 898/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 243ms/step - accuracy: 0.9855 - loss: 0.0418\n",
      "Epoch 899/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9864 - loss: 0.0265\n",
      "Epoch 900/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 243ms/step - accuracy: 0.9849 - loss: 0.0260\n",
      "Epoch 901/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 244ms/step - accuracy: 0.9849 - loss: 0.0239\n",
      "Epoch 902/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 239ms/step - accuracy: 0.9875 - loss: 0.0215\n",
      "Epoch 903/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9659 - loss: 0.0461\n",
      "Epoch 904/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 246ms/step - accuracy: 0.9521 - loss: 0.0422\n",
      "Epoch 905/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 237ms/step - accuracy: 0.9516 - loss: 0.0586\n",
      "Epoch 906/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 252ms/step - accuracy: 0.9801 - loss: 0.0214\n",
      "Epoch 907/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 238ms/step - accuracy: 0.9790 - loss: 0.0416\n",
      "Epoch 908/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 245ms/step - accuracy: 0.9938 - loss: 0.0180\n",
      "Epoch 909/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9649 - loss: 0.0606\n",
      "Epoch 910/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 247ms/step - accuracy: 0.9849 - loss: 0.0379\n",
      "Epoch 911/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 242ms/step - accuracy: 0.9803 - loss: 0.0387\n",
      "Epoch 912/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 250ms/step - accuracy: 0.9875 - loss: 0.0301\n",
      "Epoch 913/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 246ms/step - accuracy: 0.9890 - loss: 0.0187\n",
      "Epoch 914/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9825 - loss: 0.0413\n",
      "Epoch 915/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 243ms/step - accuracy: 0.9772 - loss: 0.0265\n",
      "Epoch 916/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9776 - loss: 0.0344\n",
      "Epoch 917/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9747 - loss: 0.0418\n",
      "Epoch 918/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 245ms/step - accuracy: 0.9855 - loss: 0.0318\n",
      "Epoch 919/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 247ms/step - accuracy: 0.9811 - loss: 0.0226\n",
      "Epoch 920/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 239ms/step - accuracy: 0.9757 - loss: 0.0274\n",
      "Epoch 921/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 238ms/step - accuracy: 0.9609 - loss: 0.0461\n",
      "Epoch 922/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9910 - loss: 0.0275\n",
      "Epoch 923/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 246ms/step - accuracy: 0.9476 - loss: 0.0483\n",
      "Epoch 924/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 244ms/step - accuracy: 0.9509 - loss: 0.0531\n",
      "Epoch 925/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 245ms/step - accuracy: 0.9899 - loss: 0.0134\n",
      "Epoch 926/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9731 - loss: 0.0407\n",
      "Epoch 927/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 239ms/step - accuracy: 0.9825 - loss: 0.0314\n",
      "Epoch 928/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9797 - loss: 0.0199\n",
      "Epoch 929/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 243ms/step - accuracy: 0.9803 - loss: 0.0286\n",
      "Epoch 930/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 243ms/step - accuracy: 0.9785 - loss: 0.0231\n",
      "Epoch 931/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 244ms/step - accuracy: 0.9639 - loss: 0.0351\n",
      "Epoch 932/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 242ms/step - accuracy: 0.9776 - loss: 0.0291\n",
      "Epoch 933/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 244ms/step - accuracy: 0.9910 - loss: 0.0179\n",
      "Epoch 934/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9805 - loss: 0.0197\n",
      "Epoch 935/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 243ms/step - accuracy: 0.9910 - loss: 0.0268\n",
      "Epoch 936/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 237ms/step - accuracy: 0.9875 - loss: 0.0260\n",
      "Epoch 937/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 245ms/step - accuracy: 0.9840 - loss: 0.0235\n",
      "Epoch 938/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 243ms/step - accuracy: 0.9712 - loss: 0.0424\n",
      "Epoch 939/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9740 - loss: 0.0365\n",
      "Epoch 940/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 244ms/step - accuracy: 0.9790 - loss: 0.0393\n",
      "Epoch 941/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 244ms/step - accuracy: 0.9711 - loss: 0.0370\n",
      "Epoch 942/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 244ms/step - accuracy: 0.9731 - loss: 0.0420\n",
      "Epoch 943/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 248ms/step - accuracy: 0.9594 - loss: 0.0431\n",
      "Epoch 944/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9520 - loss: 0.0454\n",
      "Epoch 945/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 246ms/step - accuracy: 0.9910 - loss: 0.0284\n",
      "Epoch 946/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9760 - loss: 0.0592\n",
      "Epoch 947/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 239ms/step - accuracy: 0.9810 - loss: 0.0287\n",
      "Epoch 948/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 238ms/step - accuracy: 0.9894 - loss: 0.0320\n",
      "Epoch 949/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 242ms/step - accuracy: 0.9747 - loss: 0.0659\n",
      "Epoch 950/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 243ms/step - accuracy: 0.9875 - loss: 0.0201\n",
      "Epoch 951/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 243ms/step - accuracy: 0.9925 - loss: 0.0396\n",
      "Epoch 952/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 237ms/step - accuracy: 0.9574 - loss: 0.0622\n",
      "Epoch 953/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 244ms/step - accuracy: 0.9923 - loss: 0.0207\n",
      "Epoch 954/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 237ms/step - accuracy: 0.9753 - loss: 0.0286\n",
      "Epoch 955/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 238ms/step - accuracy: 0.9613 - loss: 0.0534\n",
      "Epoch 956/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 244ms/step - accuracy: 0.9925 - loss: 0.0179\n",
      "Epoch 957/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9864 - loss: 0.0277\n",
      "Epoch 958/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 243ms/step - accuracy: 0.9740 - loss: 0.0318\n",
      "Epoch 959/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 243ms/step - accuracy: 0.9686 - loss: 0.0350\n",
      "Epoch 960/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 244ms/step - accuracy: 0.9569 - loss: 0.0688\n",
      "Epoch 961/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9747 - loss: 0.0459\n",
      "Epoch 962/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 243ms/step - accuracy: 0.9521 - loss: 0.0403\n",
      "Epoch 963/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 243ms/step - accuracy: 0.9574 - loss: 0.0503\n",
      "Epoch 964/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 245ms/step - accuracy: 0.9495 - loss: 0.0416\n",
      "Epoch 965/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 246ms/step - accuracy: 0.9894 - loss: 0.0359\n",
      "Epoch 966/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 239ms/step - accuracy: 0.9662 - loss: 0.0394\n",
      "Epoch 967/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9829 - loss: 0.0342\n",
      "Epoch 968/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 246ms/step - accuracy: 0.9803 - loss: 0.0451\n",
      "Epoch 969/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9903 - loss: 0.0164\n",
      "Epoch 970/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 243ms/step - accuracy: 0.9838 - loss: 0.0285\n",
      "Epoch 971/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 242ms/step - accuracy: 0.9890 - loss: 0.0198\n",
      "Epoch 972/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 244ms/step - accuracy: 0.9810 - loss: 0.0363\n",
      "Epoch 973/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 244ms/step - accuracy: 0.9688 - loss: 0.0368\n",
      "Epoch 974/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 243ms/step - accuracy: 0.9747 - loss: 0.0410\n",
      "Epoch 975/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 239ms/step - accuracy: 0.9825 - loss: 0.0292\n",
      "Epoch 976/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 245ms/step - accuracy: 0.9886 - loss: 0.0142\n",
      "Epoch 977/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 242ms/step - accuracy: 0.9910 - loss: 0.0304\n",
      "Epoch 978/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9757 - loss: 0.0304\n",
      "Epoch 979/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 242ms/step - accuracy: 0.9596 - loss: 0.0381\n",
      "Epoch 980/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 247ms/step - accuracy: 0.9890 - loss: 0.0220\n",
      "Epoch 981/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9776 - loss: 0.0338\n",
      "Epoch 982/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9737 - loss: 0.0389\n",
      "Epoch 983/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 242ms/step - accuracy: 0.9659 - loss: 0.0497\n",
      "Epoch 984/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 247ms/step - accuracy: 0.9829 - loss: 0.0320\n",
      "Epoch 985/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9675 - loss: 0.0462\n",
      "Epoch 986/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 242ms/step - accuracy: 0.9741 - loss: 0.0258\n",
      "Epoch 987/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9810 - loss: 0.0350\n",
      "Epoch 988/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 249ms/step - accuracy: 0.9801 - loss: 0.0320\n",
      "Epoch 989/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 239ms/step - accuracy: 0.9764 - loss: 0.0432\n",
      "Epoch 990/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 244ms/step - accuracy: 0.9694 - loss: 0.0391\n",
      "Epoch 991/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9437 - loss: 0.0473\n",
      "Epoch 992/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 244ms/step - accuracy: 0.9688 - loss: 0.0321\n",
      "Epoch 993/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9890 - loss: 0.0342\n",
      "Epoch 994/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9747 - loss: 0.0460\n",
      "Epoch 995/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 244ms/step - accuracy: 0.9894 - loss: 0.0222\n",
      "Epoch 996/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 243ms/step - accuracy: 0.9702 - loss: 0.0297\n",
      "Epoch 997/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9903 - loss: 0.0183\n",
      "Epoch 998/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 253ms/step - accuracy: 0.9764 - loss: 0.0320\n",
      "Epoch 999/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 253ms/step - accuracy: 0.9877 - loss: 0.0341\n",
      "Epoch 1000/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 244ms/step - accuracy: 0.9877 - loss: 0.0242\n",
      "Epoch 1001/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 244ms/step - accuracy: 0.9864 - loss: 0.0213\n",
      "Epoch 1002/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 244ms/step - accuracy: 0.9647 - loss: 0.0598\n",
      "Epoch 1003/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 243ms/step - accuracy: 0.9790 - loss: 0.0431\n",
      "Epoch 1004/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 248ms/step - accuracy: 0.9723 - loss: 0.0370\n",
      "Epoch 1005/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9894 - loss: 0.0295\n",
      "Epoch 1006/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 248ms/step - accuracy: 0.9692 - loss: 0.0387\n",
      "Epoch 1007/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 242ms/step - accuracy: 0.9622 - loss: 0.0368\n",
      "Epoch 1008/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 242ms/step - accuracy: 0.9947 - loss: 0.0300\n",
      "Epoch 1009/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 243ms/step - accuracy: 0.9864 - loss: 0.0297\n",
      "Epoch 1010/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 244ms/step - accuracy: 0.9903 - loss: 0.0236\n",
      "Epoch 1011/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9947 - loss: 0.0272\n",
      "Epoch 1012/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9894 - loss: 0.0238\n",
      "Epoch 1013/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 238ms/step - accuracy: 0.9764 - loss: 0.0328\n",
      "Epoch 1014/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 245ms/step - accuracy: 0.9516 - loss: 0.0542\n",
      "Epoch 1015/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 244ms/step - accuracy: 0.9875 - loss: 0.0260\n",
      "Epoch 1016/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9766 - loss: 0.0271\n",
      "Epoch 1017/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 244ms/step - accuracy: 0.9459 - loss: 0.0484\n",
      "Epoch 1018/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 243ms/step - accuracy: 0.9810 - loss: 0.0429\n",
      "Epoch 1019/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 242ms/step - accuracy: 0.9611 - loss: 0.0424\n",
      "Epoch 1020/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 244ms/step - accuracy: 0.9827 - loss: 0.0205\n",
      "Epoch 1021/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 237ms/step - accuracy: 0.9796 - loss: 0.0214\n",
      "Epoch 1022/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 246ms/step - accuracy: 0.9515 - loss: 0.0415\n",
      "Epoch 1023/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9495 - loss: 0.0411\n",
      "Epoch 1024/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9710 - loss: 0.0272\n",
      "Epoch 1025/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9875 - loss: 0.0287\n",
      "Epoch 1026/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 244ms/step - accuracy: 0.9764 - loss: 0.0366\n",
      "Epoch 1027/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9864 - loss: 0.0332\n",
      "Epoch 1028/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9537 - loss: 0.0416\n",
      "Epoch 1029/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9670 - loss: 0.0309\n",
      "Epoch 1030/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 245ms/step - accuracy: 0.9790 - loss: 0.0299\n",
      "Epoch 1031/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9747 - loss: 0.0511\n",
      "Epoch 1032/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 239ms/step - accuracy: 0.9747 - loss: 0.0468\n",
      "Epoch 1033/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9925 - loss: 0.0159\n",
      "Epoch 1034/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 244ms/step - accuracy: 0.9825 - loss: 0.0262\n",
      "Epoch 1035/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9476 - loss: 0.0485\n",
      "Epoch 1036/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9727 - loss: 0.0328\n",
      "Epoch 1037/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 238ms/step - accuracy: 0.9776 - loss: 0.0298\n",
      "Epoch 1038/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 244ms/step - accuracy: 0.9711 - loss: 0.0298\n",
      "Epoch 1039/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9792 - loss: 0.0229\n",
      "Epoch 1040/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 243ms/step - accuracy: 0.9788 - loss: 0.0232\n",
      "Epoch 1041/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 239ms/step - accuracy: 0.9753 - loss: 0.0308\n",
      "Epoch 1042/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 246ms/step - accuracy: 0.9886 - loss: 0.0143\n",
      "Epoch 1043/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9692 - loss: 0.0349\n",
      "Epoch 1044/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 238ms/step - accuracy: 0.9829 - loss: 0.0323\n",
      "Epoch 1045/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9864 - loss: 0.0338\n",
      "Epoch 1046/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 242ms/step - accuracy: 0.9864 - loss: 0.0331\n",
      "Epoch 1047/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 244ms/step - accuracy: 0.9831 - loss: 0.0262\n",
      "Epoch 1048/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 239ms/step - accuracy: 0.9825 - loss: 0.0284\n",
      "Epoch 1049/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 239ms/step - accuracy: 0.9938 - loss: 0.0134\n",
      "Epoch 1050/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 245ms/step - accuracy: 0.9790 - loss: 0.0348\n",
      "Epoch 1051/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9875 - loss: 0.0258\n",
      "Epoch 1052/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9877 - loss: 0.0271\n",
      "Epoch 1053/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9963 - loss: 0.0287\n",
      "Epoch 1054/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 247ms/step - accuracy: 0.9864 - loss: 0.0275\n",
      "Epoch 1055/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 253ms/step - accuracy: 0.9792 - loss: 0.0343\n",
      "Epoch 1056/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 257ms/step - accuracy: 0.9938 - loss: 0.0165\n",
      "Epoch 1057/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 243ms/step - accuracy: 0.9849 - loss: 0.0217\n",
      "Epoch 1058/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 247ms/step - accuracy: 0.9829 - loss: 0.0426\n",
      "Epoch 1059/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9772 - loss: 0.0299\n",
      "Epoch 1060/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9864 - loss: 0.0398\n",
      "Epoch 1061/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 242ms/step - accuracy: 0.9829 - loss: 0.0254\n",
      "Epoch 1062/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 244ms/step - accuracy: 0.9811 - loss: 0.0214\n",
      "Epoch 1063/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9951 - loss: 0.0135\n",
      "Epoch 1064/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9829 - loss: 0.0354\n",
      "Epoch 1065/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9810 - loss: 0.0430\n",
      "Epoch 1066/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 243ms/step - accuracy: 0.9938 - loss: 0.0164\n",
      "Epoch 1067/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 238ms/step - accuracy: 0.9849 - loss: 0.0269\n",
      "Epoch 1068/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9824 - loss: 0.0275\n",
      "Epoch 1069/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 239ms/step - accuracy: 0.9470 - loss: 0.0565\n",
      "Epoch 1070/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 243ms/step - accuracy: 0.9925 - loss: 0.0166\n",
      "Epoch 1071/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 239ms/step - accuracy: 0.9910 - loss: 0.0345\n",
      "Epoch 1072/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 242ms/step - accuracy: 0.9792 - loss: 0.0230\n",
      "Epoch 1073/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9910 - loss: 0.0216\n",
      "Epoch 1074/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 244ms/step - accuracy: 0.9622 - loss: 0.0483\n",
      "Epoch 1075/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 245ms/step - accuracy: 0.9829 - loss: 0.0410\n",
      "Epoch 1076/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9792 - loss: 0.0299\n",
      "Epoch 1077/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 244ms/step - accuracy: 0.9864 - loss: 0.0430\n",
      "Epoch 1078/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 239ms/step - accuracy: 0.9849 - loss: 0.0223\n",
      "Epoch 1079/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 242ms/step - accuracy: 0.9700 - loss: 0.0277\n",
      "Epoch 1080/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 243ms/step - accuracy: 0.9688 - loss: 0.0415\n",
      "Epoch 1081/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9548 - loss: 0.0449\n",
      "Epoch 1082/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 253ms/step - accuracy: 0.9849 - loss: 0.0390\n",
      "Epoch 1083/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 253ms/step - accuracy: 0.9866 - loss: 0.0148\n",
      "Epoch 1084/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 252ms/step - accuracy: 0.9890 - loss: 0.0287\n",
      "Epoch 1085/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 250ms/step - accuracy: 0.9925 - loss: 0.0170\n",
      "Epoch 1086/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 248ms/step - accuracy: 0.9938 - loss: 0.0181\n",
      "Epoch 1087/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 249ms/step - accuracy: 0.9838 - loss: 0.0266\n",
      "Epoch 1088/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 246ms/step - accuracy: 0.9731 - loss: 0.0307\n",
      "Epoch 1089/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 239ms/step - accuracy: 0.9764 - loss: 0.0379\n",
      "Epoch 1090/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 249ms/step - accuracy: 0.9792 - loss: 0.0345\n",
      "Epoch 1091/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 255ms/step - accuracy: 0.9890 - loss: 0.0288\n",
      "Epoch 1092/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 239ms/step - accuracy: 0.9688 - loss: 0.0406\n",
      "Epoch 1093/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9747 - loss: 0.0414\n",
      "Epoch 1094/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 249ms/step - accuracy: 0.9727 - loss: 0.0382\n",
      "Epoch 1095/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 238ms/step - accuracy: 0.9737 - loss: 0.0286\n",
      "Epoch 1096/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 239ms/step - accuracy: 0.9711 - loss: 0.0329\n",
      "Epoch 1097/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 243ms/step - accuracy: 0.9837 - loss: 0.0313\n",
      "Epoch 1098/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 244ms/step - accuracy: 0.9694 - loss: 0.0384\n",
      "Epoch 1099/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 247ms/step - accuracy: 0.9910 - loss: 0.0191\n",
      "Epoch 1100/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9667 - loss: 0.0283\n",
      "Epoch 1101/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 243ms/step - accuracy: 0.9747 - loss: 0.0531\n",
      "Epoch 1102/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9760 - loss: 0.0526\n",
      "Epoch 1103/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9569 - loss: 0.0622\n",
      "Epoch 1104/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 248ms/step - accuracy: 0.9938 - loss: 0.0162\n",
      "Epoch 1105/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9688 - loss: 0.0415\n",
      "Epoch 1106/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 242ms/step - accuracy: 0.9903 - loss: 0.0344\n",
      "Epoch 1107/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 243ms/step - accuracy: 0.9923 - loss: 0.0195\n",
      "Epoch 1108/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 244ms/step - accuracy: 0.9790 - loss: 0.0414\n",
      "Epoch 1109/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 243ms/step - accuracy: 0.9890 - loss: 0.0315\n",
      "Epoch 1110/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 238ms/step - accuracy: 0.9753 - loss: 0.0332\n",
      "Epoch 1111/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9838 - loss: 0.0273\n",
      "Epoch 1112/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 243ms/step - accuracy: 0.9732 - loss: 0.0247\n",
      "Epoch 1113/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 246ms/step - accuracy: 0.9903 - loss: 0.0350\n",
      "Epoch 1114/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 246ms/step - accuracy: 0.9855 - loss: 0.0229\n",
      "Epoch 1115/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9764 - loss: 0.0451\n",
      "Epoch 1116/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 244ms/step - accuracy: 0.9825 - loss: 0.0305\n",
      "Epoch 1117/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 242ms/step - accuracy: 0.9613 - loss: 0.0405\n",
      "Epoch 1118/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 238ms/step - accuracy: 0.9737 - loss: 0.0285\n",
      "Epoch 1119/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9760 - loss: 0.0370\n",
      "Epoch 1120/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 243ms/step - accuracy: 0.9675 - loss: 0.0357\n",
      "Epoch 1121/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 243ms/step - accuracy: 0.9790 - loss: 0.0538\n",
      "Epoch 1122/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 242ms/step - accuracy: 0.9622 - loss: 0.0494\n",
      "Epoch 1123/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 245ms/step - accuracy: 0.9910 - loss: 0.0243\n",
      "Epoch 1124/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 238ms/step - accuracy: 0.9838 - loss: 0.0280\n",
      "Epoch 1125/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 242ms/step - accuracy: 0.9864 - loss: 0.0245\n",
      "Epoch 1126/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 245ms/step - accuracy: 0.9810 - loss: 0.0419\n",
      "Epoch 1127/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9788 - loss: 0.0245\n",
      "Epoch 1128/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 242ms/step - accuracy: 0.9894 - loss: 0.0174\n",
      "Epoch 1129/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 242ms/step - accuracy: 0.9678 - loss: 0.0435\n",
      "Epoch 1130/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 243ms/step - accuracy: 0.9910 - loss: 0.0163\n",
      "Epoch 1131/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 239ms/step - accuracy: 0.9757 - loss: 0.0355\n",
      "Epoch 1132/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9864 - loss: 0.0292\n",
      "Epoch 1133/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 245ms/step - accuracy: 0.9470 - loss: 0.0585\n",
      "Epoch 1134/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9766 - loss: 0.0258\n",
      "Epoch 1135/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 239ms/step - accuracy: 0.9731 - loss: 0.0543\n",
      "Epoch 1136/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 248ms/step - accuracy: 0.9764 - loss: 0.0426\n",
      "Epoch 1137/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 243ms/step - accuracy: 0.9877 - loss: 0.0201\n",
      "Epoch 1138/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 247ms/step - accuracy: 0.9938 - loss: 0.0302\n",
      "Epoch 1139/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9910 - loss: 0.0224\n",
      "Epoch 1140/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 245ms/step - accuracy: 0.9712 - loss: 0.0452\n",
      "Epoch 1141/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9894 - loss: 0.0210\n",
      "Epoch 1142/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9875 - loss: 0.0235\n",
      "Epoch 1143/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 245ms/step - accuracy: 0.9731 - loss: 0.0442\n",
      "Epoch 1144/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 238ms/step - accuracy: 0.9657 - loss: 0.0339\n",
      "Epoch 1145/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 243ms/step - accuracy: 0.9609 - loss: 0.0353\n",
      "Epoch 1146/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 242ms/step - accuracy: 0.9790 - loss: 0.0306\n",
      "Epoch 1147/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9692 - loss: 0.0382\n",
      "Epoch 1148/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9737 - loss: 0.0336\n",
      "Epoch 1149/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 243ms/step - accuracy: 0.9675 - loss: 0.0378\n",
      "Epoch 1150/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 243ms/step - accuracy: 0.9825 - loss: 0.0299\n",
      "Epoch 1151/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 245ms/step - accuracy: 0.9864 - loss: 0.0329\n",
      "Epoch 1152/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 245ms/step - accuracy: 0.9560 - loss: 0.0449\n",
      "Epoch 1153/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 239ms/step - accuracy: 0.9688 - loss: 0.0383\n",
      "Epoch 1154/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 242ms/step - accuracy: 0.9725 - loss: 0.0337\n",
      "Epoch 1155/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 242ms/step - accuracy: 0.9647 - loss: 0.0614\n",
      "Epoch 1156/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 251ms/step - accuracy: 0.9790 - loss: 0.0421\n",
      "Epoch 1157/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 250ms/step - accuracy: 0.9772 - loss: 0.0237\n",
      "Epoch 1158/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 246ms/step - accuracy: 0.9849 - loss: 0.0263\n",
      "Epoch 1159/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 244ms/step - accuracy: 0.9857 - loss: 0.0181\n",
      "Epoch 1160/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 242ms/step - accuracy: 0.9622 - loss: 0.0345\n",
      "Epoch 1161/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9890 - loss: 0.0233\n",
      "Epoch 1162/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 242ms/step - accuracy: 0.9662 - loss: 0.0473\n",
      "Epoch 1163/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 247ms/step - accuracy: 0.9659 - loss: 0.0414\n",
      "Epoch 1164/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9829 - loss: 0.0391\n",
      "Epoch 1165/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 245ms/step - accuracy: 0.9838 - loss: 0.0322\n",
      "Epoch 1166/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 245ms/step - accuracy: 0.9712 - loss: 0.0551\n",
      "Epoch 1167/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 242ms/step - accuracy: 0.9864 - loss: 0.0365\n",
      "Epoch 1168/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 247ms/step - accuracy: 0.9711 - loss: 0.0301\n",
      "Epoch 1169/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 242ms/step - accuracy: 0.9938 - loss: 0.0147\n",
      "Epoch 1170/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 245ms/step - accuracy: 0.9910 - loss: 0.0267\n",
      "Epoch 1171/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9764 - loss: 0.0489\n",
      "Epoch 1172/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 245ms/step - accuracy: 0.9890 - loss: 0.0168\n",
      "Epoch 1173/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9825 - loss: 0.0379\n",
      "Epoch 1174/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9886 - loss: 0.0245\n",
      "Epoch 1175/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 237ms/step - accuracy: 0.9790 - loss: 0.0507\n",
      "Epoch 1176/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 246ms/step - accuracy: 0.9753 - loss: 0.0311\n",
      "Epoch 1177/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 239ms/step - accuracy: 0.9736 - loss: 0.0344\n",
      "Epoch 1178/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 242ms/step - accuracy: 0.9810 - loss: 0.0332\n",
      "Epoch 1179/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9825 - loss: 0.0394\n",
      "Epoch 1180/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 238ms/step - accuracy: 0.9790 - loss: 0.0388\n",
      "Epoch 1181/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 243ms/step - accuracy: 0.9626 - loss: 0.0414\n",
      "Epoch 1182/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 245ms/step - accuracy: 0.9825 - loss: 0.0256\n",
      "Epoch 1183/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9925 - loss: 0.0174\n",
      "Epoch 1184/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 251ms/step - accuracy: 0.9707 - loss: 0.0454\n",
      "Epoch 1185/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 239ms/step - accuracy: 0.9825 - loss: 0.0188\n",
      "Epoch 1186/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 244ms/step - accuracy: 0.9923 - loss: 0.0180\n",
      "Epoch 1187/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 238ms/step - accuracy: 0.9938 - loss: 0.0282\n",
      "Epoch 1188/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 245ms/step - accuracy: 0.9925 - loss: 0.0235\n",
      "Epoch 1189/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9864 - loss: 0.0317\n",
      "Epoch 1190/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 243ms/step - accuracy: 0.9894 - loss: 0.0245\n",
      "Epoch 1191/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9903 - loss: 0.0239\n",
      "Epoch 1192/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 245ms/step - accuracy: 0.9672 - loss: 0.0302\n",
      "Epoch 1193/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 239ms/step - accuracy: 0.9662 - loss: 0.0438\n",
      "Epoch 1194/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 243ms/step - accuracy: 0.9647 - loss: 0.0560\n",
      "Epoch 1195/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 244ms/step - accuracy: 0.9731 - loss: 0.0349\n",
      "Epoch 1196/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 244ms/step - accuracy: 0.9824 - loss: 0.0286\n",
      "Epoch 1197/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 243ms/step - accuracy: 0.9731 - loss: 0.0357\n",
      "Epoch 1198/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9622 - loss: 0.0473\n",
      "Epoch 1199/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 252ms/step - accuracy: 0.9725 - loss: 0.0517\n",
      "Epoch 1200/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 247ms/step - accuracy: 0.9747 - loss: 0.0443\n",
      "Epoch 1201/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 246ms/step - accuracy: 0.9925 - loss: 0.0156\n",
      "Epoch 1202/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 239ms/step - accuracy: 0.9849 - loss: 0.0352\n",
      "Epoch 1203/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 243ms/step - accuracy: 0.9903 - loss: 0.0362\n",
      "Epoch 1204/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 237ms/step - accuracy: 0.9864 - loss: 0.0364\n",
      "Epoch 1205/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 245ms/step - accuracy: 0.9647 - loss: 0.0639\n",
      "Epoch 1206/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 241ms/step - accuracy: 0.9853 - loss: 0.0215\n",
      "Epoch 1207/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9877 - loss: 0.0292\n",
      "Epoch 1208/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 240ms/step - accuracy: 0.9849 - loss: 0.0328\n",
      "Epoch 1209/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 238ms/step - accuracy: 0.9890 - loss: 0.0239\n",
      "Epoch 1210/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 247ms/step - accuracy: 0.9772 - loss: 0.0273\n",
      "Epoch 1211/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 239ms/step - accuracy: 0.9790 - loss: 0.0465\n",
      "Epoch 1212/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 247ms/step - accuracy: 0.9790 - loss: 0.0431\n",
      "Epoch 1213/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 242ms/step - accuracy: 0.9877 - loss: 0.0262\n",
      "Epoch 1214/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 239ms/step - accuracy: 0.9947 - loss: 0.0251\n",
      "Epoch 1215/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 243ms/step - accuracy: 0.9790 - loss: 0.0513\n",
      "Epoch 1216/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 238ms/step - accuracy: 0.9890 - loss: 0.0273\n",
      "Epoch 1217/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 249ms/step - accuracy: 0.9864 - loss: 0.0269\n",
      "Epoch 1218/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 260ms/step - accuracy: 0.9938 - loss: 0.0149\n",
      "Epoch 1219/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 242ms/step - accuracy: 0.9694 - loss: 0.0417\n",
      "Epoch 1220/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 252ms/step - accuracy: 0.9875 - loss: 0.0273\n",
      "Epoch 1221/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 232ms/step - accuracy: 0.9757 - loss: 0.0312\n",
      "Epoch 1222/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 233ms/step - accuracy: 0.9925 - loss: 0.0245\n",
      "Epoch 1223/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 226ms/step - accuracy: 0.9938 - loss: 0.0159\n",
      "Epoch 1224/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 231ms/step - accuracy: 0.9938 - loss: 0.0241\n",
      "Epoch 1225/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 233ms/step - accuracy: 0.9894 - loss: 0.0225\n",
      "Epoch 1226/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 229ms/step - accuracy: 0.9840 - loss: 0.0249\n",
      "Epoch 1227/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 232ms/step - accuracy: 0.9557 - loss: 0.0442\n",
      "Epoch 1228/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 232ms/step - accuracy: 0.9803 - loss: 0.0353\n",
      "Epoch 1229/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 865ms/step - accuracy: 0.9875 - loss: 0.0196\n",
      "Epoch 1230/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 290ms/step - accuracy: 0.9774 - loss: 0.0196\n",
      "Epoch 1231/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 289ms/step - accuracy: 0.9723 - loss: 0.0289\n",
      "Epoch 1232/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 288ms/step - accuracy: 0.9849 - loss: 0.0212\n",
      "Epoch 1233/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 290ms/step - accuracy: 0.9829 - loss: 0.0289\n",
      "Epoch 1234/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 287ms/step - accuracy: 0.9938 - loss: 0.0147\n",
      "Epoch 1235/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 288ms/step - accuracy: 0.9825 - loss: 0.0217\n",
      "Epoch 1236/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 283ms/step - accuracy: 0.9894 - loss: 0.0304\n",
      "Epoch 1237/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 295ms/step - accuracy: 0.9784 - loss: 0.0185\n",
      "Epoch 1238/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 286ms/step - accuracy: 0.9849 - loss: 0.0300\n",
      "Epoch 1239/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 286ms/step - accuracy: 0.9694 - loss: 0.0393\n",
      "Epoch 1240/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 306ms/step - accuracy: 0.9818 - loss: 0.0206\n",
      "Epoch 1241/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 304ms/step - accuracy: 0.9753 - loss: 0.0243\n",
      "Epoch 1242/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 300ms/step - accuracy: 0.9814 - loss: 0.0258\n",
      "Epoch 1243/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 285ms/step - accuracy: 0.9757 - loss: 0.0251\n",
      "Epoch 1244/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 284ms/step - accuracy: 0.9803 - loss: 0.0310\n",
      "Epoch 1245/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 289ms/step - accuracy: 0.9710 - loss: 0.0339\n",
      "Epoch 1246/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 288ms/step - accuracy: 0.9757 - loss: 0.0295\n",
      "Epoch 1247/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 281ms/step - accuracy: 0.9890 - loss: 0.0181\n",
      "Epoch 1248/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 291ms/step - accuracy: 0.9731 - loss: 0.0457\n",
      "Epoch 1249/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 278ms/step - accuracy: 0.9417 - loss: 0.0457\n",
      "Epoch 1250/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 281ms/step - accuracy: 0.9855 - loss: 0.0346\n",
      "Epoch 1251/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 276ms/step - accuracy: 0.9890 - loss: 0.0309\n",
      "Epoch 1252/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 280ms/step - accuracy: 0.9785 - loss: 0.0329\n",
      "Epoch 1253/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 281ms/step - accuracy: 0.9294 - loss: 0.0619\n",
      "Epoch 1254/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 281ms/step - accuracy: 0.9548 - loss: 0.0474\n",
      "Epoch 1255/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 284ms/step - accuracy: 0.9849 - loss: 0.0315\n",
      "Epoch 1256/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 280ms/step - accuracy: 0.9594 - loss: 0.0465\n",
      "Epoch 1257/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 284ms/step - accuracy: 0.9516 - loss: 0.0577\n",
      "Epoch 1258/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 282ms/step - accuracy: 0.9652 - loss: 0.0398\n",
      "Epoch 1259/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 284ms/step - accuracy: 0.9753 - loss: 0.0359\n",
      "Epoch 1260/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 279ms/step - accuracy: 0.9764 - loss: 0.0374\n",
      "Epoch 1261/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 282ms/step - accuracy: 0.9560 - loss: 0.0388\n",
      "Epoch 1262/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 283ms/step - accuracy: 0.9849 - loss: 0.0385\n",
      "Epoch 1263/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 287ms/step - accuracy: 0.9678 - loss: 0.0419\n",
      "Epoch 1264/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 281ms/step - accuracy: 0.9910 - loss: 0.0232\n",
      "Epoch 1265/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 280ms/step - accuracy: 0.9864 - loss: 0.0310\n",
      "Epoch 1266/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 279ms/step - accuracy: 0.9649 - loss: 0.0367\n",
      "Epoch 1267/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 284ms/step - accuracy: 0.9790 - loss: 0.0454\n",
      "Epoch 1268/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 285ms/step - accuracy: 0.9431 - loss: 0.0586\n",
      "Epoch 1269/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 279ms/step - accuracy: 0.9825 - loss: 0.0372\n",
      "Epoch 1270/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 286ms/step - accuracy: 0.9649 - loss: 0.0412\n",
      "Epoch 1271/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 284ms/step - accuracy: 0.9753 - loss: 0.0290\n",
      "Epoch 1272/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 278ms/step - accuracy: 0.9659 - loss: 0.0321\n",
      "Epoch 1273/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 283ms/step - accuracy: 0.9776 - loss: 0.0232\n",
      "Epoch 1274/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 284ms/step - accuracy: 0.9725 - loss: 0.0397\n",
      "Epoch 1275/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 281ms/step - accuracy: 0.9723 - loss: 0.0320\n",
      "Epoch 1276/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 279ms/step - accuracy: 0.9790 - loss: 0.0301\n",
      "Epoch 1277/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 281ms/step - accuracy: 0.9910 - loss: 0.0191\n",
      "Epoch 1278/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 275ms/step - accuracy: 0.9925 - loss: 0.0211\n",
      "Epoch 1279/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 279ms/step - accuracy: 0.9649 - loss: 0.0397\n",
      "Epoch 1280/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 280ms/step - accuracy: 0.9801 - loss: 0.0289\n",
      "Epoch 1281/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 280ms/step - accuracy: 0.9925 - loss: 0.0220\n",
      "Epoch 1282/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 275ms/step - accuracy: 0.9622 - loss: 0.0539\n",
      "Epoch 1283/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 280ms/step - accuracy: 0.9925 - loss: 0.0279\n",
      "Epoch 1284/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 286ms/step - accuracy: 0.9825 - loss: 0.0364\n",
      "Epoch 1285/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 286ms/step - accuracy: 0.9910 - loss: 0.0165\n",
      "Epoch 1286/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 279ms/step - accuracy: 0.9585 - loss: 0.0349\n",
      "Epoch 1287/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 279ms/step - accuracy: 0.9855 - loss: 0.0225\n",
      "Epoch 1288/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 283ms/step - accuracy: 0.9875 - loss: 0.0210\n",
      "Epoch 1289/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 280ms/step - accuracy: 0.9875 - loss: 0.0328\n",
      "Epoch 1290/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 281ms/step - accuracy: 0.9378 - loss: 0.0521\n",
      "Epoch 1291/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 283ms/step - accuracy: 0.9394 - loss: 0.0488\n",
      "Epoch 1292/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 284ms/step - accuracy: 0.9938 - loss: 0.0100\n",
      "Epoch 1293/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 281ms/step - accuracy: 0.9659 - loss: 0.0375\n",
      "Epoch 1294/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 274ms/step - accuracy: 0.9857 - loss: 0.0194\n",
      "Epoch 1295/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 285ms/step - accuracy: 0.9688 - loss: 0.0436\n",
      "Epoch 1296/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 280ms/step - accuracy: 0.9675 - loss: 0.0318\n",
      "Epoch 1297/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 280ms/step - accuracy: 0.9576 - loss: 0.0366\n",
      "Epoch 1298/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 280ms/step - accuracy: 0.9818 - loss: 0.0309\n",
      "Epoch 1299/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 281ms/step - accuracy: 0.9788 - loss: 0.0238\n",
      "Epoch 1300/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 284ms/step - accuracy: 0.9431 - loss: 0.0593\n",
      "Epoch 1301/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 278ms/step - accuracy: 0.9796 - loss: 0.0287\n",
      "Epoch 1302/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 284ms/step - accuracy: 0.9870 - loss: 0.0147\n",
      "Epoch 1303/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 277ms/step - accuracy: 0.9810 - loss: 0.0374\n",
      "Epoch 1304/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 278ms/step - accuracy: 0.9772 - loss: 0.0246\n",
      "Epoch 1305/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 283ms/step - accuracy: 0.9951 - loss: 0.0176\n",
      "Epoch 1306/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 279ms/step - accuracy: 0.9580 - loss: 0.0390\n",
      "Epoch 1307/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 279ms/step - accuracy: 0.9790 - loss: 0.0365\n",
      "Epoch 1308/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 280ms/step - accuracy: 0.9675 - loss: 0.0440\n",
      "Epoch 1309/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 289ms/step - accuracy: 0.9837 - loss: 0.0194\n",
      "Epoch 1310/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 282ms/step - accuracy: 0.9923 - loss: 0.0218\n",
      "Epoch 1311/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 274ms/step - accuracy: 0.9712 - loss: 0.0309\n",
      "Epoch 1312/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 282ms/step - accuracy: 0.9712 - loss: 0.0447\n",
      "Epoch 1313/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 276ms/step - accuracy: 0.9894 - loss: 0.0266\n",
      "Epoch 1314/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 280ms/step - accuracy: 0.9688 - loss: 0.0307\n",
      "Epoch 1315/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 277ms/step - accuracy: 0.9712 - loss: 0.0500\n",
      "Epoch 1316/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 283ms/step - accuracy: 0.9875 - loss: 0.0268\n",
      "Epoch 1317/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 278ms/step - accuracy: 0.9864 - loss: 0.0354\n",
      "Epoch 1318/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 281ms/step - accuracy: 0.9731 - loss: 0.0270\n",
      "Epoch 1319/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 273ms/step - accuracy: 0.9647 - loss: 0.0523\n",
      "Epoch 1320/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 278ms/step - accuracy: 0.9611 - loss: 0.0430\n",
      "Epoch 1321/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 286ms/step - accuracy: 0.9694 - loss: 0.0274\n",
      "Epoch 1322/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 280ms/step - accuracy: 0.9711 - loss: 0.0319\n",
      "Epoch 1323/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 276ms/step - accuracy: 0.9731 - loss: 0.0509\n",
      "Epoch 1324/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 280ms/step - accuracy: 0.9609 - loss: 0.0400\n",
      "Epoch 1325/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 276ms/step - accuracy: 0.9712 - loss: 0.0454\n",
      "Epoch 1326/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 275ms/step - accuracy: 0.9731 - loss: 0.0464\n",
      "Epoch 1327/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 283ms/step - accuracy: 0.9866 - loss: 0.0213\n",
      "Epoch 1328/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 279ms/step - accuracy: 0.9649 - loss: 0.0376\n",
      "Epoch 1329/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 274ms/step - accuracy: 0.9824 - loss: 0.0190\n",
      "Epoch 1330/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 284ms/step - accuracy: 0.9875 - loss: 0.0317\n",
      "Epoch 1331/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 278ms/step - accuracy: 0.9890 - loss: 0.0221\n",
      "Epoch 1332/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 282ms/step - accuracy: 0.9622 - loss: 0.0311\n",
      "Epoch 1333/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 281ms/step - accuracy: 0.9613 - loss: 0.0497\n",
      "Epoch 1334/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 281ms/step - accuracy: 0.9855 - loss: 0.0272\n",
      "Epoch 1335/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 282ms/step - accuracy: 0.9615 - loss: 0.0322\n",
      "Epoch 1336/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 278ms/step - accuracy: 0.9764 - loss: 0.0330\n",
      "Epoch 1337/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 284ms/step - accuracy: 0.9659 - loss: 0.0398\n",
      "Epoch 1338/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 304ms/step - accuracy: 0.9849 - loss: 0.0391\n",
      "Epoch 1339/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 292ms/step - accuracy: 0.9713 - loss: 0.0233\n",
      "Epoch 1340/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 284ms/step - accuracy: 0.9647 - loss: 0.0505\n",
      "Epoch 1341/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 283ms/step - accuracy: 0.9662 - loss: 0.0371\n",
      "Epoch 1342/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 280ms/step - accuracy: 0.9875 - loss: 0.0387\n",
      "Epoch 1343/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 278ms/step - accuracy: 0.9890 - loss: 0.0313\n",
      "Epoch 1344/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 281ms/step - accuracy: 0.9803 - loss: 0.0301\n",
      "Epoch 1345/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 277ms/step - accuracy: 0.9810 - loss: 0.0312\n",
      "Epoch 1346/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 276ms/step - accuracy: 0.9776 - loss: 0.0253\n",
      "Epoch 1347/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 281ms/step - accuracy: 0.9811 - loss: 0.0320\n",
      "Epoch 1348/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 284ms/step - accuracy: 0.9849 - loss: 0.0360\n",
      "Epoch 1349/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 277ms/step - accuracy: 0.9776 - loss: 0.0243\n",
      "Epoch 1350/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 283ms/step - accuracy: 0.9757 - loss: 0.0247\n",
      "Epoch 1351/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 279ms/step - accuracy: 0.9737 - loss: 0.0380\n",
      "Epoch 1352/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 282ms/step - accuracy: 0.9557 - loss: 0.0383\n",
      "Epoch 1353/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 277ms/step - accuracy: 0.9925 - loss: 0.0157\n",
      "Epoch 1354/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 277ms/step - accuracy: 0.9674 - loss: 0.0281\n",
      "Epoch 1355/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 281ms/step - accuracy: 0.9910 - loss: 0.0182\n",
      "Epoch 1356/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 279ms/step - accuracy: 0.9766 - loss: 0.0283\n",
      "Epoch 1357/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 279ms/step - accuracy: 0.9687 - loss: 0.0295\n",
      "Epoch 1358/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 280ms/step - accuracy: 0.9877 - loss: 0.0241\n",
      "Epoch 1359/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 281ms/step - accuracy: 0.9903 - loss: 0.0199\n",
      "Epoch 1360/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 276ms/step - accuracy: 0.9875 - loss: 0.0275\n",
      "Epoch 1361/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 282ms/step - accuracy: 0.9760 - loss: 0.0515\n",
      "Epoch 1362/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 275ms/step - accuracy: 0.9890 - loss: 0.0297\n",
      "Epoch 1363/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 282ms/step - accuracy: 0.9760 - loss: 0.0472\n",
      "Epoch 1364/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 278ms/step - accuracy: 0.9910 - loss: 0.0193\n",
      "Epoch 1365/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 279ms/step - accuracy: 0.9925 - loss: 0.0141\n",
      "Epoch 1366/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 279ms/step - accuracy: 0.9855 - loss: 0.0251\n",
      "Epoch 1367/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 275ms/step - accuracy: 0.9825 - loss: 0.0286\n",
      "Epoch 1368/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 282ms/step - accuracy: 0.9805 - loss: 0.0276\n",
      "Epoch 1369/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 281ms/step - accuracy: 0.9850 - loss: 0.0201\n",
      "Epoch 1370/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 277ms/step - accuracy: 0.9910 - loss: 0.0225\n",
      "Epoch 1371/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 279ms/step - accuracy: 0.9707 - loss: 0.0380\n",
      "Epoch 1372/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 280ms/step - accuracy: 0.9649 - loss: 0.0440\n",
      "Epoch 1373/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 276ms/step - accuracy: 0.9903 - loss: 0.0237\n",
      "Epoch 1374/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 285ms/step - accuracy: 0.9864 - loss: 0.0365\n",
      "Epoch 1375/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 279ms/step - accuracy: 0.9877 - loss: 0.0246\n",
      "Epoch 1376/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 278ms/step - accuracy: 0.9875 - loss: 0.0233\n",
      "Epoch 1377/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 280ms/step - accuracy: 0.9760 - loss: 0.0389\n",
      "Epoch 1378/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 281ms/step - accuracy: 0.9938 - loss: 0.0158\n",
      "Epoch 1379/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 280ms/step - accuracy: 0.9825 - loss: 0.0395\n",
      "Epoch 1380/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 276ms/step - accuracy: 0.9686 - loss: 0.0713\n",
      "Epoch 1381/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 271ms/step - accuracy: 0.9864 - loss: 0.0278\n",
      "Epoch 1382/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 276ms/step - accuracy: 0.9622 - loss: 0.0497\n",
      "Epoch 1383/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 276ms/step - accuracy: 0.9923 - loss: 0.0213\n",
      "Epoch 1384/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 285ms/step - accuracy: 0.9731 - loss: 0.0450\n",
      "Epoch 1385/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 279ms/step - accuracy: 0.9675 - loss: 0.0331\n",
      "Epoch 1386/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 274ms/step - accuracy: 0.9802 - loss: 0.0249\n",
      "Epoch 1387/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 281ms/step - accuracy: 0.9772 - loss: 0.0347\n",
      "Epoch 1388/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 284ms/step - accuracy: 0.9736 - loss: 0.0408\n",
      "Epoch 1389/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 279ms/step - accuracy: 0.9805 - loss: 0.0244\n",
      "Epoch 1390/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 276ms/step - accuracy: 0.9790 - loss: 0.0393\n",
      "Epoch 1391/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 276ms/step - accuracy: 0.9825 - loss: 0.0347\n",
      "Epoch 1392/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 278ms/step - accuracy: 0.9707 - loss: 0.0428\n",
      "Epoch 1393/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 281ms/step - accuracy: 0.9890 - loss: 0.0175\n",
      "Epoch 1394/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 282ms/step - accuracy: 0.9890 - loss: 0.0228\n",
      "Epoch 1395/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 276ms/step - accuracy: 0.9659 - loss: 0.0444\n",
      "Epoch 1396/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 284ms/step - accuracy: 0.9654 - loss: 0.0275\n",
      "Epoch 1397/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 278ms/step - accuracy: 0.9849 - loss: 0.0270\n",
      "Epoch 1398/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 277ms/step - accuracy: 0.9511 - loss: 0.0430\n",
      "Epoch 1399/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 276ms/step - accuracy: 0.9495 - loss: 0.0420\n",
      "Epoch 1400/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 275ms/step - accuracy: 0.9675 - loss: 0.0342\n",
      "Epoch 1401/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 276ms/step - accuracy: 0.9951 - loss: 0.0203\n",
      "Epoch 1402/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 280ms/step - accuracy: 0.9790 - loss: 0.0373\n",
      "Epoch 1403/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 283ms/step - accuracy: 0.9923 - loss: 0.0220\n",
      "Epoch 1404/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 279ms/step - accuracy: 0.9875 - loss: 0.0210\n",
      "Epoch 1405/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 285ms/step - accuracy: 0.9855 - loss: 0.0297\n",
      "Epoch 1406/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 281ms/step - accuracy: 0.9925 - loss: 0.0231\n",
      "Epoch 1407/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 276ms/step - accuracy: 0.9516 - loss: 0.0610\n",
      "Epoch 1408/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 278ms/step - accuracy: 0.9825 - loss: 0.0304\n",
      "Epoch 1409/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 278ms/step - accuracy: 0.9872 - loss: 0.0201\n",
      "Epoch 1410/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 283ms/step - accuracy: 0.9496 - loss: 0.0532\n",
      "Epoch 1411/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 278ms/step - accuracy: 0.9894 - loss: 0.0295\n",
      "Epoch 1412/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 280ms/step - accuracy: 0.9850 - loss: 0.0215\n",
      "Epoch 1413/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 277ms/step - accuracy: 0.9747 - loss: 0.0370\n",
      "Epoch 1414/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 280ms/step - accuracy: 0.9829 - loss: 0.0333\n",
      "Epoch 1415/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 276ms/step - accuracy: 0.9613 - loss: 0.0378\n",
      "Epoch 1416/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 285ms/step - accuracy: 0.9894 - loss: 0.0267\n",
      "Epoch 1417/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 277ms/step - accuracy: 0.9707 - loss: 0.0399\n",
      "Epoch 1418/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 281ms/step - accuracy: 0.9825 - loss: 0.0272\n",
      "Epoch 1419/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 278ms/step - accuracy: 0.9613 - loss: 0.0429\n",
      "Epoch 1420/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 284ms/step - accuracy: 0.9903 - loss: 0.0324\n",
      "Epoch 1421/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 280ms/step - accuracy: 0.9737 - loss: 0.0277\n",
      "Epoch 1422/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 283ms/step - accuracy: 0.9688 - loss: 0.0462\n",
      "Epoch 1423/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 277ms/step - accuracy: 0.9731 - loss: 0.0445\n",
      "Epoch 1424/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 281ms/step - accuracy: 0.9626 - loss: 0.0443\n",
      "Epoch 1425/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 281ms/step - accuracy: 0.9849 - loss: 0.0330\n",
      "Epoch 1426/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 282ms/step - accuracy: 0.9785 - loss: 0.0240\n",
      "Epoch 1427/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 283ms/step - accuracy: 0.9864 - loss: 0.0259\n",
      "Epoch 1428/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 281ms/step - accuracy: 0.9925 - loss: 0.0174\n",
      "Epoch 1429/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 285ms/step - accuracy: 0.9753 - loss: 0.0315\n",
      "Epoch 1430/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 281ms/step - accuracy: 0.9594 - loss: 0.0461\n",
      "Epoch 1431/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 283ms/step - accuracy: 0.9849 - loss: 0.0255\n",
      "Epoch 1432/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 276ms/step - accuracy: 0.9675 - loss: 0.0332\n",
      "Epoch 1433/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 286ms/step - accuracy: 0.9890 - loss: 0.0277\n",
      "Epoch 1434/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 278ms/step - accuracy: 0.9831 - loss: 0.0285\n",
      "Epoch 1435/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 282ms/step - accuracy: 0.9639 - loss: 0.0334\n",
      "Epoch 1436/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 277ms/step - accuracy: 0.9659 - loss: 0.0392\n",
      "Epoch 1437/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 284ms/step - accuracy: 0.9864 - loss: 0.0296\n",
      "Epoch 1438/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 277ms/step - accuracy: 0.9687 - loss: 0.0282\n",
      "Epoch 1439/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 282ms/step - accuracy: 0.9712 - loss: 0.0401\n",
      "Epoch 1440/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 283ms/step - accuracy: 0.9712 - loss: 0.0482\n",
      "Epoch 1441/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 288ms/step - accuracy: 0.9796 - loss: 0.0238\n",
      "Epoch 1442/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 280ms/step - accuracy: 0.9838 - loss: 0.0351\n",
      "Epoch 1443/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 279ms/step - accuracy: 0.9692 - loss: 0.0305\n",
      "Epoch 1444/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 280ms/step - accuracy: 0.9686 - loss: 0.0485\n",
      "Epoch 1445/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 280ms/step - accuracy: 0.9649 - loss: 0.0425\n",
      "Epoch 1446/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 279ms/step - accuracy: 0.9864 - loss: 0.0330\n",
      "Epoch 1447/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 279ms/step - accuracy: 0.9790 - loss: 0.0324\n",
      "Epoch 1448/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 285ms/step - accuracy: 0.9574 - loss: 0.0474\n",
      "Epoch 1449/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 282ms/step - accuracy: 0.9600 - loss: 0.0347\n",
      "Epoch 1450/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 284ms/step - accuracy: 0.9750 - loss: 0.0298\n",
      "Epoch 1451/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 280ms/step - accuracy: 0.9827 - loss: 0.0204\n",
      "Epoch 1452/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 282ms/step - accuracy: 0.9753 - loss: 0.0349\n",
      "Epoch 1453/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 279ms/step - accuracy: 0.9675 - loss: 0.0369\n",
      "Epoch 1454/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 284ms/step - accuracy: 0.9541 - loss: 0.0397\n",
      "Epoch 1455/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 281ms/step - accuracy: 0.9925 - loss: 0.0256\n",
      "Epoch 1456/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 280ms/step - accuracy: 0.9925 - loss: 0.0144\n",
      "Epoch 1457/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 277ms/step - accuracy: 0.9894 - loss: 0.0340\n",
      "Epoch 1458/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 280ms/step - accuracy: 0.9875 - loss: 0.0304\n",
      "Epoch 1459/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 277ms/step - accuracy: 0.9675 - loss: 0.0392\n",
      "Epoch 1460/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 279ms/step - accuracy: 0.9923 - loss: 0.0201\n",
      "Epoch 1461/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 277ms/step - accuracy: 0.9712 - loss: 0.0344\n",
      "Epoch 1462/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 275ms/step - accuracy: 0.9764 - loss: 0.0462\n",
      "Epoch 1463/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 280ms/step - accuracy: 0.9925 - loss: 0.0207\n",
      "Epoch 1464/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 274ms/step - accuracy: 0.9659 - loss: 0.0473\n",
      "Epoch 1465/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 280ms/step - accuracy: 0.9521 - loss: 0.0472\n",
      "Epoch 1466/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 280ms/step - accuracy: 0.9711 - loss: 0.0303\n",
      "Epoch 1467/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 280ms/step - accuracy: 0.9938 - loss: 0.0144\n",
      "Epoch 1468/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 281ms/step - accuracy: 0.9686 - loss: 0.0500\n",
      "Epoch 1469/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 279ms/step - accuracy: 0.9890 - loss: 0.0188\n",
      "Epoch 1470/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 281ms/step - accuracy: 0.9711 - loss: 0.0336\n",
      "Epoch 1471/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 278ms/step - accuracy: 0.9829 - loss: 0.0281\n",
      "Epoch 1472/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 285ms/step - accuracy: 0.9792 - loss: 0.0323\n",
      "Epoch 1473/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 277ms/step - accuracy: 0.9639 - loss: 0.0410\n",
      "Epoch 1474/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 276ms/step - accuracy: 0.9727 - loss: 0.0314\n",
      "Epoch 1475/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 284ms/step - accuracy: 0.9766 - loss: 0.0314\n",
      "Epoch 1476/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 277ms/step - accuracy: 0.9923 - loss: 0.0305\n",
      "Epoch 1477/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 277ms/step - accuracy: 0.9803 - loss: 0.0369\n",
      "Epoch 1478/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 284ms/step - accuracy: 0.9790 - loss: 0.0363\n",
      "Epoch 1479/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 277ms/step - accuracy: 0.9910 - loss: 0.0222\n",
      "Epoch 1480/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 280ms/step - accuracy: 0.9890 - loss: 0.0264\n",
      "Epoch 1481/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 273ms/step - accuracy: 0.9537 - loss: 0.0451\n",
      "Epoch 1482/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 283ms/step - accuracy: 0.9825 - loss: 0.0253\n",
      "Epoch 1483/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 281ms/step - accuracy: 0.9753 - loss: 0.0252\n",
      "Epoch 1484/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 275ms/step - accuracy: 0.9450 - loss: 0.0447\n",
      "Epoch 1485/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 275ms/step - accuracy: 0.9594 - loss: 0.0430\n",
      "Epoch 1486/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 284ms/step - accuracy: 0.9818 - loss: 0.0315\n",
      "Epoch 1487/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 277ms/step - accuracy: 0.9723 - loss: 0.0360\n",
      "Epoch 1488/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 280ms/step - accuracy: 0.9790 - loss: 0.0363\n",
      "Epoch 1489/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 278ms/step - accuracy: 0.9598 - loss: 0.0437\n",
      "Epoch 1490/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 282ms/step - accuracy: 0.9910 - loss: 0.0269\n",
      "Epoch 1491/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 275ms/step - accuracy: 0.9731 - loss: 0.0569\n",
      "Epoch 1492/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 285ms/step - accuracy: 0.9727 - loss: 0.0373\n",
      "Epoch 1493/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 277ms/step - accuracy: 0.9903 - loss: 0.0261\n",
      "Epoch 1494/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 285ms/step - accuracy: 0.9825 - loss: 0.0280\n",
      "Epoch 1495/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 278ms/step - accuracy: 0.9803 - loss: 0.0360\n",
      "Epoch 1496/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 285ms/step - accuracy: 0.9857 - loss: 0.0207\n",
      "Epoch 1497/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 279ms/step - accuracy: 0.9688 - loss: 0.0409\n",
      "Epoch 1498/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 275ms/step - accuracy: 0.9925 - loss: 0.0295\n",
      "Epoch 1499/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 284ms/step - accuracy: 0.9810 - loss: 0.0401\n",
      "Epoch 1500/1500\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 275ms/step - accuracy: 0.9711 - loss: 0.0322\n"
     ]
    }
   ],
   "source": [
    "# Train the model and store the history\n",
    "history = model.fit(train_x, train_y, epochs=1500, batch_size=16, verbose=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save the model\n",
    "model.save('data/output/Attention/Rasika.keras')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done\n"
     ]
    }
   ],
   "source": [
    "# Print \"Done\"\n",
    "print(\"Done\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x600 with 0 Axes>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x600 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot training history\n",
    "plt.figure(figsize=(12, 6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot training accuracy\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(history.history['accuracy'])\n",
    "plt.title('Model Accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend(['Train'], loc='upper left')\n",
    "\n",
    "# Plot training loss\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(history.history['loss'])\n",
    "plt.title('Model Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend(['Train'], loc='upper left')\n",
    "\n",
    "# Show plots\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  }
 ],
 "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": "3.12.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}