ProjectCode.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "f4964135",
   "metadata": {},
   "outputs": [],
   "source": [
    "import librosa\n",
    "import librosa.display\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "545f82cc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: librosa in c:\\users\\shweta\\anaconda3\\lib\\site-packages (0.10.0.post2)Note: you may need to restart the kernel to use updated packages.\n",
      "\n",
      "Requirement already satisfied: pooch<1.7,>=1.0 in c:\\users\\shweta\\anaconda3\\lib\\site-packages (from librosa) (1.6.0)\n",
      "Requirement already satisfied: soxr>=0.3.2 in c:\\users\\shweta\\anaconda3\\lib\\site-packages (from librosa) (0.3.5)\n",
      "Requirement already satisfied: numba>=0.51.0 in c:\\users\\shweta\\anaconda3\\lib\\site-packages (from librosa) (0.55.1)\n",
      "Requirement already satisfied: typing-extensions>=4.1.1 in c:\\users\\shweta\\anaconda3\\lib\\site-packages (from librosa) (4.1.1)\n",
      "Requirement already satisfied: audioread>=2.1.9 in c:\\users\\shweta\\anaconda3\\lib\\site-packages (from librosa) (3.0.0)\n",
      "Requirement already satisfied: msgpack>=1.0 in c:\\users\\shweta\\anaconda3\\lib\\site-packages (from librosa) (1.0.2)\n",
      "Requirement already satisfied: numpy!=1.22.0,!=1.22.1,!=1.22.2,>=1.20.3 in c:\\users\\shweta\\anaconda3\\lib\\site-packages (from librosa) (1.21.5)\n",
      "Requirement already satisfied: scikit-learn>=0.20.0 in c:\\users\\shweta\\anaconda3\\lib\\site-packages (from librosa) (1.2.2)\n",
      "Requirement already satisfied: decorator>=4.3.0 in c:\\users\\shweta\\anaconda3\\lib\\site-packages (from librosa) (5.1.1)\n",
      "Requirement already satisfied: joblib>=0.14 in c:\\users\\shweta\\anaconda3\\lib\\site-packages (from librosa) (1.2.0)\n",
      "Requirement already satisfied: soundfile>=0.12.1 in c:\\users\\shweta\\anaconda3\\lib\\site-packages (from librosa) (0.12.1)\n",
      "Requirement already satisfied: lazy-loader>=0.1 in c:\\users\\shweta\\anaconda3\\lib\\site-packages (from librosa) (0.2)\n",
      "Requirement already satisfied: scipy>=1.2.0 in c:\\users\\shweta\\anaconda3\\lib\\site-packages (from librosa) (1.7.3)\n",
      "Requirement already satisfied: setuptools in c:\\users\\shweta\\anaconda3\\lib\\site-packages (from numba>=0.51.0->librosa) (67.6.1)\n",
      "Requirement already satisfied: llvmlite<0.39,>=0.38.0rc1 in c:\\users\\shweta\\anaconda3\\lib\\site-packages (from numba>=0.51.0->librosa) (0.38.0)\n",
      "Requirement already satisfied: packaging>=20.0 in c:\\users\\shweta\\anaconda3\\lib\\site-packages (from pooch<1.7,>=1.0->librosa) (21.3)\n",
      "Requirement already satisfied: requests>=2.19.0 in c:\\users\\shweta\\anaconda3\\lib\\site-packages (from pooch<1.7,>=1.0->librosa) (2.27.1)\n",
      "Requirement already satisfied: appdirs>=1.3.0 in c:\\users\\shweta\\anaconda3\\lib\\site-packages (from pooch<1.7,>=1.0->librosa) (1.4.4)\n",
      "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in c:\\users\\shweta\\anaconda3\\lib\\site-packages (from packaging>=20.0->pooch<1.7,>=1.0->librosa) (3.0.4)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\shweta\\anaconda3\\lib\\site-packages (from requests>=2.19.0->pooch<1.7,>=1.0->librosa) (2021.10.8)\n",
      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in c:\\users\\shweta\\anaconda3\\lib\\site-packages (from requests>=2.19.0->pooch<1.7,>=1.0->librosa) (1.26.9)\n",
      "Requirement already satisfied: charset-normalizer~=2.0.0 in c:\\users\\shweta\\anaconda3\\lib\\site-packages (from requests>=2.19.0->pooch<1.7,>=1.0->librosa) (2.0.4)\n",
      "Requirement already satisfied: idna<4,>=2.5 in c:\\users\\shweta\\anaconda3\\lib\\site-packages (from requests>=2.19.0->pooch<1.7,>=1.0->librosa) (3.3)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in c:\\users\\shweta\\anaconda3\\lib\\site-packages (from scikit-learn>=0.20.0->librosa) (2.2.0)\n",
      "Requirement already satisfied: cffi>=1.0 in c:\\users\\shweta\\anaconda3\\lib\\site-packages (from soundfile>=0.12.1->librosa) (1.15.0)\n",
      "Requirement already satisfied: pycparser in c:\\users\\shweta\\anaconda3\\lib\\site-packages (from cffi>=1.0->soundfile>=0.12.1->librosa) (2.21)\n"
     ]
    }
   ],
   "source": [
    "pip install librosa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "c3e69a6c",
   "metadata": {},
   "outputs": [],
   "source": [
    "my_path = 'C:/Users/Shweta/Downloads/NLP'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "ee58b762",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is the len of our data:  1440\n",
      "this is an example of a wave file: 03-01-01-01-02-01-03.wav\n",
      "this index position represents the emotion label: 01\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "my_data = os.listdir(my_path)\n",
    "print(\"this is the len of our data: \", len(my_data))\n",
    "print(\"this is an example of a wave file:\", my_data[50])\n",
    "print(\"this index position represents the emotion label:\", my_data[50][6:8])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "c308a1e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "data, sampling_rate = librosa.load(my_path+'/03-01-01-01-02-01-03.wav')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "c4871dad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ERROR: Could not find a version that satisfies the requirement pyplot (from versions: none)\n",
      "ERROR: No matching distribution found for pyplot\n"
     ]
    }
   ],
   "source": [
    "pip install pyplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "ae6bcbc5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: python_speech_features in c:\\users\\shweta\\anaconda3\\lib\\site-packages (0.6)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install python_speech_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "81e3c6af",
   "metadata": {},
   "outputs": [],
   "source": [
    "from python_speech_features import mfcc\n",
    "import scipy.io.wavfile\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "emotions_num = [\"Neutral-01\",\"Calm-02\",\"Happy-03\",\"Sad-04\",\"Angry-05\",\"Fearful-06\",\"Disgust-07\",\"Surprised-08\"]\n",
    "actors = [\"01\",\"02\",\"03\",\"04\",\"05\",\"06\",\"07\",\"08\",\"09\",\"10\",\"11\",\"12\",\n",
    "         \"13\",\"14\",\"15\",\"16\",\"17\",\"18\",\"19\",\"20\",\"21\",\"22\",\"23\",\"24\"]\n",
    "\n",
    "\n",
    "def mfcc_extraction(path, modal_vocal, emotions_num, actors):\n",
    "    labels = []\n",
    "    mfcc_features = []\n",
    "    \n",
    "    #with repetition number: 1 and statement number 1 and emotional intensity number #1\n",
    "    for index1,emotion_num in enumerate(emotions_num):\n",
    "        for index,actor in enumerate(actors):\n",
    "            sig, rate = librosa.load(path+modal_vocal+emotion_num[-2:]+\"-01-01-01-\"+actor+\".wav\", sr=16000)\n",
    "            mfcc_feat = mfcc(sig,rate)\n",
    "            mfcc_features.append(mfcc_feat)\n",
    "            \n",
    "            labels.append(emotion_num[:-3])\n",
    "    \n",
    "    #with repetition number: 1 and statement number 2 and emotional intensity number 1\n",
    "    for index1,emotion_num in enumerate(emotions_num):\n",
    "        for index,actor in enumerate(actors):\n",
    "            sig, rate = librosa.load(path+modal_vocal+emotion_num[-2:]+\"-01-02-01-\"+actor+\".wav\", sr=16000)\n",
    "            mfcc_feat = mfcc(sig,rate)\n",
    "            mfcc_features.append(mfcc_feat)\n",
    "            \n",
    "            labels.append(emotion_num[:-3])\n",
    "            \n",
    "    #with repetition number: 2 and statement number 1 and emotional intensity number #1\n",
    "    for index1,emotion_num in enumerate(emotions_num):\n",
    "        for index,actor in enumerate(actors):\n",
    "            sig, rate = librosa.load(path+modal_vocal+emotion_num[-2:]+\"-01-01-02-\"+actor+\".wav\", sr=16000)\n",
    "            mfcc_feat = mfcc(sig,rate)\n",
    "            mfcc_features.append(mfcc_feat)\n",
    "            \n",
    "            labels.append(emotion_num[:-3])\n",
    "            \n",
    "    #with repetition number: 2 and statement number 2 and emotional intensity number 1\n",
    "    for index1,emotion_num in enumerate(emotions_num):\n",
    "        for index,actor in enumerate(actors):\n",
    "            sig, rate = librosa.load(path+modal_vocal+emotion_num[-2:]+\"-01-02-02-\"+actor+\".wav\", sr=16000)\n",
    "            mfcc_feat = mfcc(sig,rate)\n",
    "            mfcc_features.append(mfcc_feat)\n",
    "            \n",
    "            labels.append(emotion_num[:-3])\n",
    "    \n",
    "    #with repetition number: 1 and statement number 1 and emotional intensity number #2\n",
    "    for index1,emotion_num in enumerate(emotions_num):\n",
    "        for index,actor in enumerate(actors):\n",
    "            if emotion_num != \"Neutral-01\":\n",
    "                sig, rate = librosa.load(path+modal_vocal+emotion_num[-2:]+\"-02-01-01-\"+actor+\".wav\", sr=16000)\n",
    "                mfcc_feat = mfcc(sig,rate)\n",
    "                mfcc_features.append(mfcc_feat)\n",
    "                \n",
    "                labels.append(emotion_num[:-3])\n",
    "    \n",
    "    #with repetition number: 1 and statement number 2 and emotional intensity number 2\n",
    "    for index1,emotion_num in enumerate(emotions_num):\n",
    "        for index,actor in enumerate(actors):\n",
    "            if emotion_num != \"Neutral-01\":\n",
    "                sig, rate = librosa.load(path+modal_vocal+emotion_num[-2:]+\"-02-02-01-\"+actor+\".wav\", sr=16000)\n",
    "                mfcc_feat = mfcc(sig,rate)\n",
    "                mfcc_features.append(mfcc_feat)\n",
    "                \n",
    "                labels.append(emotion_num[:-3])\n",
    "            \n",
    "    #with repetition number: 2 and statement number 1 and emotional intensity number #2\n",
    "    for index1,emotion_num in enumerate(emotions_num):\n",
    "        for index,actor in enumerate(actors):\n",
    "            if emotion_num != \"Neutral-01\":\n",
    "                sig, rate = librosa.load(path+modal_vocal+emotion_num[-2:]+\"-02-01-02-\"+actor+\".wav\", sr=16000)\n",
    "                mfcc_feat = mfcc(sig,rate)\n",
    "                mfcc_features.append(mfcc_feat)\n",
    "                \n",
    "                labels.append(emotion_num[:-3])\n",
    "            \n",
    "    #with repetition number: 2 and statement number 2 and emotional intensity number 2\n",
    "    for index1,emotion_num in enumerate(emotions_num):\n",
    "        for index,actor in enumerate(actors):\n",
    "            if emotion_num != \"Neutral-01\":\n",
    "                sig, rate = librosa.load(path+modal_vocal+emotion_num[-2:]+\"-02-02-02-\"+actor+\".wav\", sr=16000)\n",
    "                mfcc_feat = mfcc(sig,rate)\n",
    "                mfcc_features.append(mfcc_feat)\n",
    "                \n",
    "                labels.append(emotion_num[:-3])      \n",
    "\n",
    "    return mfcc_features, labels\n",
    "\n",
    "\n",
    "mfcc_features_and_labels = mfcc_extraction(my_path,'/03-01-', emotions_num, actors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "df047db5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "len of examples: 1440\n",
      "len of labels: 1440\n",
      "\n",
      "feature dimension: 2\n",
      "feature shape: (363, 13)\n",
      "\n",
      "[[-21.80529784 -32.13530495   1.87810235 ...  15.86349969 -12.30932477\n",
      "   -2.54386672]\n",
      " [-53.00045216 -35.32816489  -3.6113983  ...   4.34525362   5.4657722\n",
      "    3.42755866]\n",
      " [-23.15053697 -35.71724609 -11.26693056 ...  -5.27451964  -3.92387835\n",
      "   -4.1406193 ]\n",
      " ...\n",
      " [-20.11279977 -29.75572129  -0.10945783 ...   4.76921003   3.28228248\n",
      "   -1.23155094]\n",
      " [-22.16631442 -31.38104127  -8.23737611 ...  -0.46860924   2.58914949\n",
      "  -10.55278512]\n",
      " [-53.11562463 -28.39883025  -3.56797779 ...  -2.66300537  -5.32838607\n",
      "   -4.62758576]] Neutral\n"
     ]
    }
   ],
   "source": [
    "# exract features and labels from \n",
    "mfcc_features, mfcc_labels = mfcc_features_and_labels\n",
    "print(\"len of examples:\", len(mfcc_features))\n",
    "print(\"len of labels:\", len(mfcc_labels))\n",
    "print()\n",
    "print(\"feature dimension:\", mfcc_features[0].ndim)\n",
    "print(\"feature shape:\", mfcc_features[1].shape)\n",
    "print()\n",
    "mfcc_labels = np.array(mfcc_labels)\n",
    "print(mfcc_features[0],mfcc_labels[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "e642387c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def zero_pad(features):\n",
    "    \n",
    "    new_spoken_train = []\n",
    "    max_shape = 0\n",
    "    for x in features:\n",
    "        all_shapes = x.shape[0]\n",
    "        if all_shapes > max_shape:\n",
    "            max_shape = all_shapes\n",
    "\n",
    "    for x in features:\n",
    "        difference = max_shape-x.shape[0]\n",
    "        zero_padded = np.pad(x,((0,difference),(0,0)), \"constant\")\n",
    "        new_spoken_train.append(zero_padded)\n",
    "\n",
    "    new_spoken_train = np.array(new_spoken_train)\n",
    "    \n",
    "    return new_spoken_train\n",
    "\n",
    "mfcc_features = zero_pad(mfcc_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "a9a5cb7b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1440, 526, 13)\n",
      "6838\n"
     ]
    }
   ],
   "source": [
    "print(mfcc_features.shape)\n",
    "print(526*13)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "9deb59e4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1440, 6838)\n"
     ]
    }
   ],
   "source": [
    "flatten_mfcc_features = np.reshape(mfcc_features,(mfcc_features.shape[0],6838))\n",
    "print(flatten_mfcc_features.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "c54ba185",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "data = flatten_mfcc_features\n",
    "scaler = StandardScaler()\n",
    "mfcc_features = scaler.fit_transform(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "48e77abf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Neutral': 96, 'Calm': 192, 'Happy': 192, 'Sad': 192, 'Angry': 192, 'Fearful': 192, 'Disgust': 192, 'Surprised': 192}\n"
     ]
    }
   ],
   "source": [
    "dictionary = {}\n",
    "for i in mfcc_labels:\n",
    "    dictionary[i] = dictionary.get(i,0) + 1\n",
    "\n",
    "print(dictionary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "71a74032",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Note: you may need to restart the kernel to use updated packages.Requirement already satisfied: imblearn in c:\\users\\shweta\\anaconda3\\lib\\site-packages (0.0)\n",
      "\n",
      "Requirement already satisfied: imbalanced-learn in c:\\users\\shweta\\anaconda3\\lib\\site-packages (from imblearn) (0.10.1)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in c:\\users\\shweta\\anaconda3\\lib\\site-packages (from imbalanced-learn->imblearn) (2.2.0)\n",
      "Requirement already satisfied: scikit-learn>=1.0.2 in c:\\users\\shweta\\anaconda3\\lib\\site-packages (from imbalanced-learn->imblearn) (1.2.2)\n",
      "Requirement already satisfied: numpy>=1.17.3 in c:\\users\\shweta\\anaconda3\\lib\\site-packages (from imbalanced-learn->imblearn) (1.21.5)\n",
      "Requirement already satisfied: joblib>=1.1.1 in c:\\users\\shweta\\anaconda3\\lib\\site-packages (from imbalanced-learn->imblearn) (1.2.0)\n",
      "Requirement already satisfied: scipy>=1.3.2 in c:\\users\\shweta\\anaconda3\\lib\\site-packages (from imbalanced-learn->imblearn) (1.7.3)\n"
     ]
    }
   ],
   "source": [
    "pip install imblearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "9a824aff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Neutral': 192, 'Calm': 192, 'Happy': 192, 'Sad': 192, 'Angry': 192, 'Fearful': 192, 'Disgust': 192, 'Surprised': 192}\n"
     ]
    }
   ],
   "source": [
    "from imblearn.over_sampling import RandomOverSampler\n",
    "ros = RandomOverSampler(random_state=42)\n",
    "new_mfcc_features,  mfcc_labels = ros.fit_resample(mfcc_features,  mfcc_labels)\n",
    "\n",
    "dictionary_2 = {}\n",
    "for i in mfcc_labels:\n",
    "    dictionary_2[i] = dictionary_2.get(i,0) + 1\n",
    "\n",
    "print(dictionary_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "8d5446b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_mfcc_features = np.reshape(new_mfcc_features,(new_mfcc_features.shape[0],526, 13))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "8e4523b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1536, 526, 13)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_mfcc_features.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "f313e071",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(new_mfcc_features, mfcc_labels, test_size=0.30, \n",
    "                                                    random_state=42, stratify = mfcc_labels, shuffle = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "dd5c1331",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1075, 8)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Shweta\\anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:868: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "from numpy import array\n",
    "from numpy import argmax\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "\n",
    "# define example\n",
    "y_train = y_train\n",
    "y_train_values = array(y_train)\n",
    "# integer encode\n",
    "y_train_label_encoder = LabelEncoder()\n",
    "y_train_integer_encoded = y_train_label_encoder.fit_transform(y_train_values)\n",
    "# binary encode\n",
    "y_train_onehot_encoder = OneHotEncoder(sparse=False)\n",
    "y_train_integer_encoded = y_train_integer_encoded.reshape(len(y_train_integer_encoded), 1)\n",
    "y_train_onehot_encoded = y_train_onehot_encoder.fit_transform(y_train_integer_encoded)\n",
    "print(y_train_onehot_encoded.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "17272439",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(461, 8)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Shweta\\anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:868: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "# define example\n",
    "y_test = y_test\n",
    "y_test_values = array(y_test)\n",
    "# integer encode\n",
    "y_test_label_encoder = LabelEncoder()\n",
    "y_test_integer_encoded = y_test_label_encoder.fit_transform(y_test_values)\n",
    "# binary encode\n",
    "y_test_onehot_encoder = OneHotEncoder(sparse=False)\n",
    "y_test_integer_encoded = y_test_integer_encoded.reshape(len(y_test_integer_encoded), 1)\n",
    "y_test_onehot_encoded = y_test_onehot_encoder.fit_transform(y_test_integer_encoded)\n",
    "print(y_test_onehot_encoded.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "3f3f7054",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1075, 526, 13)\n",
      "(1075, 8)\n",
      "(461, 526, 13)\n",
      "(461, 8)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape)\n",
    "print(y_train_onehot_encoded.shape)\n",
    "print(X_test.shape)\n",
    "print(y_test_onehot_encoded.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "92ab1da8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Note: you may need to restart the kernel to use updated packages.Requirement already satisfied: keras in c:\\users\\shweta\\anaconda3\\lib\\site-packages (2.11.0)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "pip install keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "ad6368c9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: tensorflow in c:\\users\\shweta\\anaconda3\\lib\\site-packages (2.11.0)Note: you may need to restart the kernel to use updated packages.\n",
      "\n",
      "Requirement already satisfied: tensorflow-intel==2.11.0 in c:\\users\\shweta\\anaconda3\\lib\\site-packages (from tensorflow) (2.11.0)\n",
      "Requirement already satisfied: tensorboard<2.12,>=2.11 in c:\\users\\shweta\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.11.0->tensorflow) (2.11.2)\n",
      "Requirement already satisfied: libclang>=13.0.0 in c:\\users\\shweta\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.11.0->tensorflow) (15.0.6.1)\n",
      "Requirement already satisfied: six>=1.12.0 in c:\\users\\shweta\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.11.0->tensorflow) (1.16.0)\n",
      "Requirement already satisfied: packaging in c:\\users\\shweta\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.11.0->tensorflow) (21.3)\n",
      "Requirement already satisfied: termcolor>=1.1.0 in c:\\users\\shweta\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.11.0->tensorflow) (2.2.0)\n",
      "Requirement already satisfied: typing-extensions>=3.6.6 in c:\\users\\shweta\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.11.0->tensorflow) (4.1.1)\n",
      "Requirement already satisfied: wrapt>=1.11.0 in c:\\users\\shweta\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.11.0->tensorflow) (1.12.1)\n",
      "Requirement already satisfied: flatbuffers>=2.0 in c:\\users\\shweta\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.11.0->tensorflow) (23.3.3)\n",
      "Requirement already satisfied: protobuf<3.20,>=3.9.2 in c:\\users\\shweta\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.11.0->tensorflow) (3.19.1)\n",
      "Requirement already satisfied: google-pasta>=0.1.1 in c:\\users\\shweta\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.11.0->tensorflow) (0.2.0)\n",
      "Requirement already satisfied: h5py>=2.9.0 in c:\\users\\shweta\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.11.0->tensorflow) (3.6.0)\n",
      "Requirement already satisfied: tensorflow-estimator<2.12,>=2.11.0 in c:\\users\\shweta\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.11.0->tensorflow) (2.11.0)\n",
      "Requirement already satisfied: tensorflow-io-gcs-filesystem>=0.23.1 in c:\\users\\shweta\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.11.0->tensorflow) (0.31.0)\n",
      "Requirement already satisfied: astunparse>=1.6.0 in c:\\users\\shweta\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.11.0->tensorflow) (1.6.3)\n",
      "Requirement already satisfied: numpy>=1.20 in c:\\users\\shweta\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.11.0->tensorflow) (1.21.5)\n",
      "Requirement already satisfied: gast<=0.4.0,>=0.2.1 in c:\\users\\shweta\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.11.0->tensorflow) (0.4.0)\n",
      "Requirement already satisfied: keras<2.12,>=2.11.0 in c:\\users\\shweta\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.11.0->tensorflow) (2.11.0)\n",
      "Requirement already satisfied: setuptools in c:\\users\\shweta\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.11.0->tensorflow) (67.6.1)\n",
      "Requirement already satisfied: grpcio<2.0,>=1.24.3 in c:\\users\\shweta\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.11.0->tensorflow) (1.42.0)\n",
      "Requirement already satisfied: absl-py>=1.0.0 in c:\\users\\shweta\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.11.0->tensorflow) (1.4.0)\n",
      "Requirement already satisfied: opt-einsum>=2.3.2 in c:\\users\\shweta\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.11.0->tensorflow) (3.3.0)\n",
      "Requirement already satisfied: wheel<1.0,>=0.23.0 in c:\\users\\shweta\\anaconda3\\lib\\site-packages (from astunparse>=1.6.0->tensorflow-intel==2.11.0->tensorflow) (0.37.1)\n",
      "Requirement already satisfied: tensorboard-plugin-wit>=1.6.0 in c:\\users\\shweta\\anaconda3\\lib\\site-packages (from tensorboard<2.12,>=2.11->tensorflow-intel==2.11.0->tensorflow) (1.8.1)\n",
      "Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in c:\\users\\shweta\\anaconda3\\lib\\site-packages (from tensorboard<2.12,>=2.11->tensorflow-intel==2.11.0->tensorflow) (0.4.6)\n",
      "Requirement already satisfied: markdown>=2.6.8 in c:\\users\\shweta\\anaconda3\\lib\\site-packages (from tensorboard<2.12,>=2.11->tensorflow-intel==2.11.0->tensorflow) (3.3.4)\n",
      "Requirement already satisfied: requests<3,>=2.21.0 in c:\\users\\shweta\\anaconda3\\lib\\site-packages (from tensorboard<2.12,>=2.11->tensorflow-intel==2.11.0->tensorflow) (2.27.1)\n",
      "Requirement already satisfied: google-auth<3,>=1.6.3 in c:\\users\\shweta\\anaconda3\\lib\\site-packages (from tensorboard<2.12,>=2.11->tensorflow-intel==2.11.0->tensorflow) (1.33.0)\n",
      "Requirement already satisfied: werkzeug>=1.0.1 in c:\\users\\shweta\\anaconda3\\lib\\site-packages (from tensorboard<2.12,>=2.11->tensorflow-intel==2.11.0->tensorflow) (2.0.3)\n",
      "Requirement already satisfied: tensorboard-data-server<0.7.0,>=0.6.0 in c:\\users\\shweta\\anaconda3\\lib\\site-packages (from tensorboard<2.12,>=2.11->tensorflow-intel==2.11.0->tensorflow) (0.6.1)\n",
      "Requirement already satisfied: pyasn1-modules>=0.2.1 in c:\\users\\shweta\\anaconda3\\lib\\site-packages (from google-auth<3,>=1.6.3->tensorboard<2.12,>=2.11->tensorflow-intel==2.11.0->tensorflow) (0.2.8)\n",
      "Requirement already satisfied: rsa<5,>=3.1.4 in c:\\users\\shweta\\anaconda3\\lib\\site-packages (from google-auth<3,>=1.6.3->tensorboard<2.12,>=2.11->tensorflow-intel==2.11.0->tensorflow) (4.7.2)\n",
      "Requirement already satisfied: cachetools<5.0,>=2.0.0 in c:\\users\\shweta\\anaconda3\\lib\\site-packages (from google-auth<3,>=1.6.3->tensorboard<2.12,>=2.11->tensorflow-intel==2.11.0->tensorflow) (4.2.2)\n",
      "Requirement already satisfied: requests-oauthlib>=0.7.0 in c:\\users\\shweta\\anaconda3\\lib\\site-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.12,>=2.11->tensorflow-intel==2.11.0->tensorflow) (1.3.1)\n",
      "Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in c:\\users\\shweta\\anaconda3\\lib\\site-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard<2.12,>=2.11->tensorflow-intel==2.11.0->tensorflow) (0.4.8)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\shweta\\anaconda3\\lib\\site-packages (from requests<3,>=2.21.0->tensorboard<2.12,>=2.11->tensorflow-intel==2.11.0->tensorflow) (2021.10.8)\n",
      "Requirement already satisfied: idna<4,>=2.5 in c:\\users\\shweta\\anaconda3\\lib\\site-packages (from requests<3,>=2.21.0->tensorboard<2.12,>=2.11->tensorflow-intel==2.11.0->tensorflow) (3.3)\n",
      "Requirement already satisfied: charset-normalizer~=2.0.0 in c:\\users\\shweta\\anaconda3\\lib\\site-packages (from requests<3,>=2.21.0->tensorboard<2.12,>=2.11->tensorflow-intel==2.11.0->tensorflow) (2.0.4)\n",
      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in c:\\users\\shweta\\anaconda3\\lib\\site-packages (from requests<3,>=2.21.0->tensorboard<2.12,>=2.11->tensorflow-intel==2.11.0->tensorflow) (1.26.9)\n",
      "Requirement already satisfied: oauthlib>=3.0.0 in c:\\users\\shweta\\anaconda3\\lib\\site-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.12,>=2.11->tensorflow-intel==2.11.0->tensorflow) (3.2.2)\n",
      "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in c:\\users\\shweta\\anaconda3\\lib\\site-packages (from packaging->tensorflow-intel==2.11.0->tensorflow) (3.0.4)\n"
     ]
    }
   ],
   "source": [
    "pip install tensorflow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "df76beda",
   "metadata": {},
   "outputs": [],
   "source": [
    "import keras\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Embedding\n",
    "from keras.layers import Input, Flatten, Dropout, Activation\n",
    "from keras.layers import Conv1D, MaxPooling1D\n",
    "from keras.models import Model\n",
    "from keras.callbacks import ModelCheckpoint\n",
    "\n",
    "len_of_labels = len(y_train_onehot_encoded[0])\n",
    "model = Sequential()\n",
    "\n",
    "model.add(Conv1D(128, 4, padding='same', input_shape=(X_train.shape[1],X_train.shape[2])))\n",
    "model.add(Activation('relu'))\n",
    "model.add(MaxPooling1D(pool_size=(2)))\n",
    "model.add(Conv1D(128, 4, padding='same',))\n",
    "model.add(Activation('relu'))\n",
    "model.add(Dropout(0.4))\n",
    "model.add(MaxPooling1D(pool_size=(2)))\n",
    "model.add(Conv1D(128, 4, padding='same',))\n",
    "model.add(Activation('relu'))\n",
    "model.add(Dropout(0.4))\n",
    "model.add(MaxPooling1D(pool_size=(2)))\n",
    "model.add(Dropout(0.4))\n",
    "model.add(Flatten())\n",
    "model.add(Dense(len_of_labels))\n",
    "model.add(Activation('softmax'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "1e664c99",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_6\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv1d_3 (Conv1D)           (None, 526, 128)          6784      \n",
      "                                                                 \n",
      " activation_4 (Activation)   (None, 526, 128)          0         \n",
      "                                                                 \n",
      " max_pooling1d_3 (MaxPooling  (None, 263, 128)         0         \n",
      " 1D)                                                             \n",
      "                                                                 \n",
      " conv1d_4 (Conv1D)           (None, 263, 128)          65664     \n",
      "                                                                 \n",
      " activation_5 (Activation)   (None, 263, 128)          0         \n",
      "                                                                 \n",
      " dropout_3 (Dropout)         (None, 263, 128)          0         \n",
      "                                                                 \n",
      " max_pooling1d_4 (MaxPooling  (None, 131, 128)         0         \n",
      " 1D)                                                             \n",
      "                                                                 \n",
      " conv1d_5 (Conv1D)           (None, 131, 128)          65664     \n",
      "                                                                 \n",
      " activation_6 (Activation)   (None, 131, 128)          0         \n",
      "                                                                 \n",
      " dropout_4 (Dropout)         (None, 131, 128)          0         \n",
      "                                                                 \n",
      " max_pooling1d_5 (MaxPooling  (None, 65, 128)          0         \n",
      " 1D)                                                             \n",
      "                                                                 \n",
      " dropout_5 (Dropout)         (None, 65, 128)           0         \n",
      "                                                                 \n",
      " flatten_1 (Flatten)         (None, 8320)              0         \n",
      "                                                                 \n",
      " dense_5 (Dense)             (None, 8)                 66568     \n",
      "                                                                 \n",
      " activation_7 (Activation)   (None, 8)                 0         \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 204,680\n",
      "Trainable params: 204,680\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "2274e4eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Shweta\\anaconda3\\lib\\site-packages\\keras\\optimizers\\optimizer_v2\\rmsprop.py:143: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
      "  super().__init__(name, **kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "17/17 [==============================] - 6s 253ms/step - loss: 2.1084 - accuracy: 0.2242 - val_loss: 1.8015 - val_accuracy: 0.3774\n",
      "Epoch 2/50\n",
      "17/17 [==============================] - 5s 289ms/step - loss: 1.6006 - accuracy: 0.4009 - val_loss: 1.6288 - val_accuracy: 0.3557\n",
      "Epoch 3/50\n",
      "17/17 [==============================] - 6s 326ms/step - loss: 1.3490 - accuracy: 0.5051 - val_loss: 1.4743 - val_accuracy: 0.4599\n",
      "Epoch 4/50\n",
      "17/17 [==============================] - 4s 254ms/step - loss: 1.1879 - accuracy: 0.5572 - val_loss: 1.3853 - val_accuracy: 0.5206\n",
      "Epoch 5/50\n",
      "17/17 [==============================] - 4s 224ms/step - loss: 1.0412 - accuracy: 0.6177 - val_loss: 1.4021 - val_accuracy: 0.5358\n",
      "Epoch 6/50\n",
      "17/17 [==============================] - 3s 205ms/step - loss: 0.8724 - accuracy: 0.6995 - val_loss: 1.2365 - val_accuracy: 0.5683\n",
      "Epoch 7/50\n",
      "17/17 [==============================] - 4s 212ms/step - loss: 0.7112 - accuracy: 0.7609 - val_loss: 1.3529 - val_accuracy: 0.5163\n",
      "Epoch 8/50\n",
      "17/17 [==============================] - 3s 185ms/step - loss: 0.6278 - accuracy: 0.7842 - val_loss: 1.2193 - val_accuracy: 0.5640\n",
      "Epoch 9/50\n",
      "17/17 [==============================] - 3s 189ms/step - loss: 0.5479 - accuracy: 0.7963 - val_loss: 1.2359 - val_accuracy: 0.5423\n",
      "Epoch 10/50\n",
      "17/17 [==============================] - 3s 203ms/step - loss: 0.4895 - accuracy: 0.8419 - val_loss: 1.0298 - val_accuracy: 0.6161\n",
      "Epoch 11/50\n",
      "17/17 [==============================] - 3s 179ms/step - loss: 0.3618 - accuracy: 0.8781 - val_loss: 0.9953 - val_accuracy: 0.6443\n",
      "Epoch 12/50\n",
      "17/17 [==============================] - 3s 193ms/step - loss: 0.3605 - accuracy: 0.8874 - val_loss: 1.1094 - val_accuracy: 0.5900\n",
      "Epoch 13/50\n",
      "17/17 [==============================] - 3s 179ms/step - loss: 0.2877 - accuracy: 0.8902 - val_loss: 0.9764 - val_accuracy: 0.6399\n",
      "Epoch 14/50\n",
      "17/17 [==============================] - 3s 199ms/step - loss: 0.2576 - accuracy: 0.9051 - val_loss: 1.0091 - val_accuracy: 0.6204\n",
      "Epoch 15/50\n",
      "17/17 [==============================] - 3s 196ms/step - loss: 0.1997 - accuracy: 0.9312 - val_loss: 1.0049 - val_accuracy: 0.6291\n",
      "Epoch 16/50\n",
      "17/17 [==============================] - 4s 229ms/step - loss: 0.2318 - accuracy: 0.9209 - val_loss: 1.1321 - val_accuracy: 0.5987\n",
      "Epoch 17/50\n",
      "17/17 [==============================] - 3s 204ms/step - loss: 0.1672 - accuracy: 0.9423 - val_loss: 1.0011 - val_accuracy: 0.6226\n",
      "Epoch 18/50\n",
      "17/17 [==============================] - 3s 191ms/step - loss: 0.1991 - accuracy: 0.9312 - val_loss: 1.0794 - val_accuracy: 0.6182\n",
      "Epoch 19/50\n",
      "17/17 [==============================] - 3s 173ms/step - loss: 0.1171 - accuracy: 0.9581 - val_loss: 1.0531 - val_accuracy: 0.6291\n",
      "Epoch 20/50\n",
      "17/17 [==============================] - 3s 175ms/step - loss: 0.1450 - accuracy: 0.9563 - val_loss: 1.0114 - val_accuracy: 0.6616\n",
      "Epoch 21/50\n",
      "17/17 [==============================] - 3s 173ms/step - loss: 0.1553 - accuracy: 0.9553 - val_loss: 0.9955 - val_accuracy: 0.6486\n",
      "Epoch 22/50\n",
      "17/17 [==============================] - 3s 178ms/step - loss: 0.1032 - accuracy: 0.9647 - val_loss: 1.0191 - val_accuracy: 0.6681\n",
      "Epoch 23/50\n",
      "17/17 [==============================] - 3s 191ms/step - loss: 0.1539 - accuracy: 0.9488 - val_loss: 1.0236 - val_accuracy: 0.6594\n",
      "Epoch 24/50\n",
      "17/17 [==============================] - 3s 182ms/step - loss: 0.1201 - accuracy: 0.9535 - val_loss: 1.0115 - val_accuracy: 0.6681\n",
      "Epoch 25/50\n",
      "17/17 [==============================] - 3s 204ms/step - loss: 0.0968 - accuracy: 0.9693 - val_loss: 1.0058 - val_accuracy: 0.6573\n",
      "Epoch 26/50\n",
      "17/17 [==============================] - 3s 201ms/step - loss: 0.1217 - accuracy: 0.9609 - val_loss: 1.1879 - val_accuracy: 0.6182\n",
      "Epoch 27/50\n",
      "17/17 [==============================] - 3s 187ms/step - loss: 0.0893 - accuracy: 0.9665 - val_loss: 0.9616 - val_accuracy: 0.6703\n",
      "Epoch 28/50\n",
      "17/17 [==============================] - 3s 195ms/step - loss: 0.0976 - accuracy: 0.9665 - val_loss: 1.0652 - val_accuracy: 0.6529\n",
      "Epoch 29/50\n",
      "17/17 [==============================] - 3s 207ms/step - loss: 0.1014 - accuracy: 0.9656 - val_loss: 1.1302 - val_accuracy: 0.6464\n",
      "Epoch 30/50\n",
      "17/17 [==============================] - 3s 188ms/step - loss: 0.0666 - accuracy: 0.9777 - val_loss: 1.0405 - val_accuracy: 0.6681\n",
      "Epoch 31/50\n",
      "17/17 [==============================] - 3s 202ms/step - loss: 0.0614 - accuracy: 0.9795 - val_loss: 1.0419 - val_accuracy: 0.6876\n",
      "Epoch 32/50\n",
      "17/17 [==============================] - 3s 197ms/step - loss: 0.1009 - accuracy: 0.9684 - val_loss: 1.1046 - val_accuracy: 0.6443\n",
      "Epoch 33/50\n",
      "17/17 [==============================] - 4s 209ms/step - loss: 0.0349 - accuracy: 0.9888 - val_loss: 1.2399 - val_accuracy: 0.6594\n",
      "Epoch 34/50\n",
      "17/17 [==============================] - 4s 215ms/step - loss: 0.1021 - accuracy: 0.9702 - val_loss: 1.1920 - val_accuracy: 0.6399\n",
      "Epoch 35/50\n",
      "17/17 [==============================] - 3s 209ms/step - loss: 0.0701 - accuracy: 0.9730 - val_loss: 1.2380 - val_accuracy: 0.6399\n",
      "Epoch 36/50\n",
      "17/17 [==============================] - 3s 199ms/step - loss: 0.0767 - accuracy: 0.9749 - val_loss: 1.1131 - val_accuracy: 0.6464\n",
      "Epoch 37/50\n",
      "17/17 [==============================] - 4s 219ms/step - loss: 0.0412 - accuracy: 0.9879 - val_loss: 1.3388 - val_accuracy: 0.6182\n",
      "Epoch 38/50\n",
      "17/17 [==============================] - 4s 203ms/step - loss: 0.0539 - accuracy: 0.9870 - val_loss: 1.1996 - val_accuracy: 0.6638\n",
      "Epoch 39/50\n",
      "17/17 [==============================] - 3s 184ms/step - loss: 0.0591 - accuracy: 0.9814 - val_loss: 1.3292 - val_accuracy: 0.6508\n",
      "Epoch 40/50\n",
      "17/17 [==============================] - 3s 197ms/step - loss: 0.0574 - accuracy: 0.9823 - val_loss: 1.2636 - val_accuracy: 0.6703\n",
      "Epoch 41/50\n",
      "17/17 [==============================] - 4s 220ms/step - loss: 0.0541 - accuracy: 0.9860 - val_loss: 1.2353 - val_accuracy: 0.6551\n",
      "Epoch 42/50\n",
      "17/17 [==============================] - 3s 205ms/step - loss: 0.0680 - accuracy: 0.9767 - val_loss: 1.2858 - val_accuracy: 0.6443\n",
      "Epoch 43/50\n",
      "17/17 [==============================] - 4s 215ms/step - loss: 0.0247 - accuracy: 0.9953 - val_loss: 1.5757 - val_accuracy: 0.6312\n",
      "Epoch 44/50\n",
      "17/17 [==============================] - 3s 191ms/step - loss: 0.0856 - accuracy: 0.9702 - val_loss: 1.2597 - val_accuracy: 0.6443\n",
      "Epoch 45/50\n",
      "17/17 [==============================] - 3s 198ms/step - loss: 0.0677 - accuracy: 0.9823 - val_loss: 1.3722 - val_accuracy: 0.6464\n",
      "Epoch 46/50\n",
      "17/17 [==============================] - 3s 206ms/step - loss: 0.0661 - accuracy: 0.9767 - val_loss: 1.3178 - val_accuracy: 0.6508\n",
      "Epoch 47/50\n",
      "17/17 [==============================] - 3s 201ms/step - loss: 0.0572 - accuracy: 0.9860 - val_loss: 1.3583 - val_accuracy: 0.6659\n",
      "Epoch 48/50\n",
      "17/17 [==============================] - 3s 192ms/step - loss: 0.0280 - accuracy: 0.9916 - val_loss: 1.4041 - val_accuracy: 0.6573\n",
      "Epoch 49/50\n",
      "17/17 [==============================] - 3s 180ms/step - loss: 0.0626 - accuracy: 0.9777 - val_loss: 1.3856 - val_accuracy: 0.6703\n",
      "Epoch 50/50\n",
      "17/17 [==============================] - 3s 184ms/step - loss: 0.0292 - accuracy: 0.9898 - val_loss: 1.3979 - val_accuracy: 0.6573\n"
     ]
    }
   ],
   "source": [
    "from keras.optimizers import RMSprop\n",
    "\n",
    "model.compile(loss= \"categorical_crossentropy\",\n",
    "              optimizer= RMSprop(lr=0.001),\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "cnnhistory= model.fit(X_train, y_train_onehot_encoded,batch_size= 64, epochs= 50,validation_data=(X_test, y_test_onehot_encoded))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "8d853ca9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15/15 [==============================] - 0s 18ms/step - loss: 1.3979 - accuracy: 0.6573\n",
      "validation accuracy: 0.6572667956352234\n"
     ]
    }
   ],
   "source": [
    "result = model.evaluate(X_test, y_test_onehot_encoded)\n",
    "print(\"validation accuracy:\", result[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "015ffde3",
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import Sequential\n",
    "from keras import layers\n",
    "from keras import optimizers\n",
    "from keras import callbacks "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "3d3a4827",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_16\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " lstm_24 (LSTM)              (None, 526, 64)           19968     \n",
      "                                                                 \n",
      " lstm_25 (LSTM)              (None, 64)                33024     \n",
      "                                                                 \n",
      " dense_16 (Dense)            (None, 8)                 520       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 53,512\n",
      "Trainable params: 53,512\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n",
      "Epoch 1/400\n",
      "47/47 [==============================] - 37s 680ms/step - loss: 2.0819 - categorical_accuracy: 0.1135 - val_loss: 2.0797 - val_categorical_accuracy: 0.1236 - lr: 0.0010\n",
      "Epoch 2/400\n",
      "47/47 [==============================] - 31s 656ms/step - loss: 2.0808 - categorical_accuracy: 0.1079 - val_loss: 2.0797 - val_categorical_accuracy: 0.1258 - lr: 0.0010\n",
      "Epoch 3/400\n",
      "47/47 [==============================] - 29s 620ms/step - loss: 2.0805 - categorical_accuracy: 0.1070 - val_loss: 2.0799 - val_categorical_accuracy: 0.1258 - lr: 0.0010\n",
      "Epoch 4/400\n",
      "47/47 [==============================] - 29s 609ms/step - loss: 2.0807 - categorical_accuracy: 0.1219 - val_loss: 2.0797 - val_categorical_accuracy: 0.1236 - lr: 0.0010\n",
      "Epoch 5/400\n",
      "47/47 [==============================] - 29s 623ms/step - loss: 2.0798 - categorical_accuracy: 0.1116 - val_loss: 2.0797 - val_categorical_accuracy: 0.1236 - lr: 0.0010\n",
      "Epoch 6/400\n",
      "47/47 [==============================] - 30s 635ms/step - loss: 2.0800 - categorical_accuracy: 0.1126 - val_loss: 2.0798 - val_categorical_accuracy: 0.1236 - lr: 0.0010\n",
      "Epoch 7/400\n",
      "47/47 [==============================] - 31s 660ms/step - loss: 2.0795 - categorical_accuracy: 0.1126 - val_loss: 2.0799 - val_categorical_accuracy: 0.1236 - lr: 0.0010\n",
      "Epoch 8/400\n",
      "47/47 [==============================] - 30s 647ms/step - loss: 2.0794 - categorical_accuracy: 0.1181 - val_loss: 2.0801 - val_categorical_accuracy: 0.1236 - lr: 0.0010\n",
      "Epoch 9/400\n",
      "47/47 [==============================] - 32s 685ms/step - loss: 2.0793 - categorical_accuracy: 0.1098 - val_loss: 2.0800 - val_categorical_accuracy: 0.1236 - lr: 0.0010\n",
      "Epoch 10/400\n",
      "47/47 [==============================] - 30s 650ms/step - loss: 2.0790 - categorical_accuracy: 0.1116 - val_loss: 2.0802 - val_categorical_accuracy: 0.1236 - lr: 0.0010\n",
      "Epoch 11/400\n",
      "47/47 [==============================] - 30s 644ms/step - loss: 2.0790 - categorical_accuracy: 0.1219 - val_loss: 2.0802 - val_categorical_accuracy: 0.1236 - lr: 0.0010\n",
      "Epoch 12/400\n",
      "47/47 [==============================] - 31s 657ms/step - loss: 2.0784 - categorical_accuracy: 0.1116 - val_loss: 2.0800 - val_categorical_accuracy: 0.1258 - lr: 0.0010\n",
      "Epoch 13/400\n",
      "47/47 [==============================] - 30s 649ms/step - loss: 2.0778 - categorical_accuracy: 0.1051 - val_loss: 2.0770 - val_categorical_accuracy: 0.1236 - lr: 0.0010\n",
      "Epoch 14/400\n",
      "47/47 [==============================] - 30s 650ms/step - loss: 2.0935 - categorical_accuracy: 0.1284 - val_loss: 2.0898 - val_categorical_accuracy: 0.1302 - lr: 0.0010\n",
      "Epoch 15/400\n",
      "47/47 [==============================] - 32s 684ms/step - loss: 2.0845 - categorical_accuracy: 0.1265 - val_loss: 2.0712 - val_categorical_accuracy: 0.1562 - lr: 0.0010\n",
      "Epoch 16/400\n",
      "47/47 [==============================] - 31s 654ms/step - loss: 2.0579 - categorical_accuracy: 0.1702 - val_loss: 2.0425 - val_categorical_accuracy: 0.1735 - lr: 0.0010\n",
      "Epoch 17/400\n",
      "47/47 [==============================] - 31s 663ms/step - loss: 2.0354 - categorical_accuracy: 0.1795 - val_loss: 2.0611 - val_categorical_accuracy: 0.1670 - lr: 0.0010\n",
      "Epoch 18/400\n",
      "47/47 [==============================] - 31s 659ms/step - loss: 2.0398 - categorical_accuracy: 0.1665 - val_loss: 2.0433 - val_categorical_accuracy: 0.1627 - lr: 0.0010\n",
      "Epoch 19/400\n",
      "47/47 [==============================] - 31s 659ms/step - loss: 2.0342 - categorical_accuracy: 0.1758 - val_loss: 2.0440 - val_categorical_accuracy: 0.1605 - lr: 0.0010\n",
      "Epoch 20/400\n",
      "47/47 [==============================] - 32s 690ms/step - loss: 2.0348 - categorical_accuracy: 0.1833 - val_loss: 2.0518 - val_categorical_accuracy: 0.1649 - lr: 0.0010\n",
      "Epoch 21/400\n",
      "47/47 [==============================] - 31s 669ms/step - loss: 2.0685 - categorical_accuracy: 0.1470 - val_loss: 2.0617 - val_categorical_accuracy: 0.1627 - lr: 0.0010\n",
      "Epoch 22/400\n",
      "47/47 [==============================] - 31s 655ms/step - loss: 2.0374 - categorical_accuracy: 0.1656 - val_loss: 1.9748 - val_categorical_accuracy: 0.2061 - lr: 0.0010\n",
      "Epoch 23/400\n",
      "47/47 [==============================] - 27s 567ms/step - loss: 2.0669 - categorical_accuracy: 0.1526 - val_loss: 2.0800 - val_categorical_accuracy: 0.1323 - lr: 0.0010\n",
      "Epoch 24/400\n",
      "47/47 [==============================] - 18s 392ms/step - loss: 2.0874 - categorical_accuracy: 0.1367 - val_loss: 2.0924 - val_categorical_accuracy: 0.1280 - lr: 0.0010\n",
      "Epoch 25/400\n",
      "47/47 [==============================] - 18s 392ms/step - loss: 2.0833 - categorical_accuracy: 0.1256 - val_loss: 2.1135 - val_categorical_accuracy: 0.1345 - lr: 0.0010\n",
      "Epoch 26/400\n",
      "47/47 [==============================] - 19s 396ms/step - loss: 2.0954 - categorical_accuracy: 0.1377 - val_loss: 2.0962 - val_categorical_accuracy: 0.1258 - lr: 0.0010\n",
      "Epoch 27/400\n",
      "47/47 [==============================] - 18s 394ms/step - loss: 2.0922 - categorical_accuracy: 0.1321 - val_loss: 2.0739 - val_categorical_accuracy: 0.1410 - lr: 0.0010\n",
      "Epoch 28/400\n",
      "47/47 [==============================] - 19s 411ms/step - loss: 2.0825 - categorical_accuracy: 0.1358 - val_loss: 2.0668 - val_categorical_accuracy: 0.1453 - lr: 0.0010\n",
      "Epoch 29/400\n",
      "47/47 [==============================] - 19s 398ms/step - loss: 2.0741 - categorical_accuracy: 0.1367 - val_loss: 2.0694 - val_categorical_accuracy: 0.1432 - lr: 0.0010\n",
      "Epoch 30/400\n",
      "47/47 [==============================] - 19s 399ms/step - loss: 2.0724 - categorical_accuracy: 0.1377 - val_loss: 2.0750 - val_categorical_accuracy: 0.1388 - lr: 0.0010\n",
      "Epoch 31/400\n",
      "47/47 [==============================] - 19s 401ms/step - loss: 2.0825 - categorical_accuracy: 0.1256 - val_loss: 2.0901 - val_categorical_accuracy: 0.1258 - lr: 0.0010\n",
      "Epoch 32/400\n",
      "47/47 [==============================] - 19s 397ms/step - loss: 2.0853 - categorical_accuracy: 0.1284 - val_loss: 2.0875 - val_categorical_accuracy: 0.1258 - lr: 0.0010\n",
      "Epoch 33/400\n",
      "47/47 [==============================] - 20s 417ms/step - loss: 2.0865 - categorical_accuracy: 0.1237 - val_loss: 2.0835 - val_categorical_accuracy: 0.1258 - lr: 0.0010\n",
      "Epoch 34/400\n",
      "47/47 [==============================] - 19s 403ms/step - loss: 2.0858 - categorical_accuracy: 0.1181 - val_loss: 2.0796 - val_categorical_accuracy: 0.1280 - lr: 0.0010\n",
      "Epoch 35/400\n",
      "47/47 [==============================] - 19s 402ms/step - loss: 2.0811 - categorical_accuracy: 0.1237 - val_loss: 2.0803 - val_categorical_accuracy: 0.1280 - lr: 0.0010\n",
      "Epoch 36/400\n",
      "47/47 [==============================] - 20s 431ms/step - loss: 2.0824 - categorical_accuracy: 0.1247 - val_loss: 2.0757 - val_categorical_accuracy: 0.1453 - lr: 0.0010\n",
      "Epoch 37/400\n",
      "47/47 [==============================] - 19s 400ms/step - loss: 2.0751 - categorical_accuracy: 0.1405 - val_loss: 2.0676 - val_categorical_accuracy: 0.1367 - lr: 0.0010\n",
      "Epoch 38/400\n",
      "47/47 [==============================] - 19s 404ms/step - loss: 2.0693 - categorical_accuracy: 0.1395 - val_loss: 2.0595 - val_categorical_accuracy: 0.1562 - lr: 0.0010\n",
      "Epoch 39/400\n",
      "47/47 [==============================] - 19s 403ms/step - loss: 2.0626 - categorical_accuracy: 0.1433 - val_loss: 2.0715 - val_categorical_accuracy: 0.1649 - lr: 0.0010\n",
      "Epoch 40/400\n",
      "47/47 [==============================] - 19s 404ms/step - loss: 2.0532 - categorical_accuracy: 0.1619 - val_loss: 2.0271 - val_categorical_accuracy: 0.1931 - lr: 0.0010\n",
      "Epoch 41/400\n",
      "47/47 [==============================] - 20s 426ms/step - loss: 2.0339 - categorical_accuracy: 0.1786 - val_loss: 2.0178 - val_categorical_accuracy: 0.2169 - lr: 0.0010\n",
      "Epoch 42/400\n",
      "47/47 [==============================] - 19s 396ms/step - loss: 2.0256 - categorical_accuracy: 0.1916 - val_loss: 2.2745 - val_categorical_accuracy: 0.1757 - lr: 0.0010\n",
      "Epoch 43/400\n",
      "47/47 [==============================] - 19s 404ms/step - loss: 2.1126 - categorical_accuracy: 0.1219 - val_loss: 2.0940 - val_categorical_accuracy: 0.1323 - lr: 0.0010\n",
      "Epoch 44/400\n",
      "47/47 [==============================] - 19s 403ms/step - loss: 2.0967 - categorical_accuracy: 0.1256 - val_loss: 2.0787 - val_categorical_accuracy: 0.1258 - lr: 0.0010\n",
      "Epoch 45/400\n",
      "47/47 [==============================] - 19s 400ms/step - loss: 2.0908 - categorical_accuracy: 0.1405 - val_loss: 2.0877 - val_categorical_accuracy: 0.1258 - lr: 0.0010\n",
      "Epoch 46/400\n",
      "47/47 [==============================] - 19s 409ms/step - loss: 2.0936 - categorical_accuracy: 0.1153 - val_loss: 2.0770 - val_categorical_accuracy: 0.1258 - lr: 0.0010\n",
      "Epoch 47/400\n",
      "47/47 [==============================] - 19s 403ms/step - loss: 2.0808 - categorical_accuracy: 0.1302 - val_loss: 2.0490 - val_categorical_accuracy: 0.1670 - lr: 0.0010\n",
      "Epoch 48/400\n",
      "47/47 [==============================] - 22s 479ms/step - loss: 2.0895 - categorical_accuracy: 0.1116 - val_loss: 2.0812 - val_categorical_accuracy: 0.1236 - lr: 0.0010\n",
      "Epoch 49/400\n",
      "47/47 [==============================] - 23s 482ms/step - loss: 2.0891 - categorical_accuracy: 0.1172 - val_loss: 2.0823 - val_categorical_accuracy: 0.1258 - lr: 0.0010\n",
      "Epoch 50/400\n",
      "47/47 [==============================] - 23s 481ms/step - loss: 2.0861 - categorical_accuracy: 0.1284 - val_loss: 2.0914 - val_categorical_accuracy: 0.1258 - lr: 0.0010\n",
      "Epoch 51/400\n",
      "47/47 [==============================] - 23s 484ms/step - loss: 2.0872 - categorical_accuracy: 0.1219 - val_loss: 2.0858 - val_categorical_accuracy: 0.1258 - lr: 0.0010\n",
      "Epoch 52/400\n",
      "47/47 [==============================] - 22s 477ms/step - loss: 2.0855 - categorical_accuracy: 0.1107 - val_loss: 2.0832 - val_categorical_accuracy: 0.1236 - lr: 0.0010\n",
      "Epoch 53/400\n",
      "47/47 [==============================] - 23s 496ms/step - loss: 2.0840 - categorical_accuracy: 0.1126 - val_loss: 2.0833 - val_categorical_accuracy: 0.1258 - lr: 0.0010\n",
      "Epoch 54/400\n",
      "47/47 [==============================] - 23s 500ms/step - loss: 2.0805 - categorical_accuracy: 0.1274 - val_loss: 2.1377 - val_categorical_accuracy: 0.1518 - lr: 0.0010\n",
      "Epoch 55/400\n",
      "47/47 [==============================] - 23s 485ms/step - loss: 2.0806 - categorical_accuracy: 0.1479 - val_loss: 2.0587 - val_categorical_accuracy: 0.1410 - lr: 0.0010\n",
      "Epoch 56/400\n",
      "47/47 [==============================] - 22s 479ms/step - loss: 2.0701 - categorical_accuracy: 0.1321 - val_loss: 2.0466 - val_categorical_accuracy: 0.1714 - lr: 0.0010\n",
      "Epoch 57/400\n",
      "47/47 [==============================] - 23s 481ms/step - loss: 2.0552 - categorical_accuracy: 0.1814 - val_loss: 2.0717 - val_categorical_accuracy: 0.1735 - lr: 0.0010\n",
      "Epoch 58/400\n",
      "47/47 [==============================] - 23s 482ms/step - loss: 2.0453 - categorical_accuracy: 0.1805 - val_loss: 2.0295 - val_categorical_accuracy: 0.1909 - lr: 0.0010\n",
      "Epoch 59/400\n",
      "47/47 [==============================] - 23s 485ms/step - loss: 2.0351 - categorical_accuracy: 0.1591 - val_loss: 2.0273 - val_categorical_accuracy: 0.1670 - lr: 0.0010\n",
      "Epoch 60/400\n",
      "47/47 [==============================] - 23s 484ms/step - loss: 2.0229 - categorical_accuracy: 0.1786 - val_loss: 1.9933 - val_categorical_accuracy: 0.1996 - lr: 0.0010\n",
      "Epoch 61/400\n",
      "47/47 [==============================] - 22s 476ms/step - loss: 2.0252 - categorical_accuracy: 0.1870 - val_loss: 2.0143 - val_categorical_accuracy: 0.1887 - lr: 0.0010\n",
      "Epoch 62/400\n",
      "47/47 [==============================] - 22s 476ms/step - loss: 2.0152 - categorical_accuracy: 0.1684 - val_loss: 2.0237 - val_categorical_accuracy: 0.1584 - lr: 0.0010\n",
      "Epoch 63/400\n",
      "47/47 [==============================] - 22s 478ms/step - loss: 2.0183 - categorical_accuracy: 0.1647 - val_loss: 2.0156 - val_categorical_accuracy: 0.1909 - lr: 0.0010\n",
      "Epoch 64/400\n",
      "47/47 [==============================] - 23s 480ms/step - loss: 1.9987 - categorical_accuracy: 0.1963 - val_loss: 1.9831 - val_categorical_accuracy: 0.2017 - lr: 0.0010\n",
      "Epoch 65/400\n",
      "47/47 [==============================] - 22s 475ms/step - loss: 1.9930 - categorical_accuracy: 0.1842 - val_loss: 1.9887 - val_categorical_accuracy: 0.1931 - lr: 0.0010\n",
      "Epoch 66/400\n",
      "47/47 [==============================] - 22s 476ms/step - loss: 2.0146 - categorical_accuracy: 0.1814 - val_loss: 1.9695 - val_categorical_accuracy: 0.2017 - lr: 0.0010\n",
      "Epoch 67/400\n",
      "47/47 [==============================] - 22s 478ms/step - loss: 1.9760 - categorical_accuracy: 0.2130 - val_loss: 1.9371 - val_categorical_accuracy: 0.2191 - lr: 0.0010\n",
      "Epoch 68/400\n",
      "47/47 [==============================] - 23s 483ms/step - loss: 1.9939 - categorical_accuracy: 0.1981 - val_loss: 1.9715 - val_categorical_accuracy: 0.2039 - lr: 0.0010\n",
      "Epoch 69/400\n",
      "47/47 [==============================] - 22s 478ms/step - loss: 1.9916 - categorical_accuracy: 0.1823 - val_loss: 1.9953 - val_categorical_accuracy: 0.1931 - lr: 0.0010\n",
      "Epoch 70/400\n",
      "47/47 [==============================] - 24s 505ms/step - loss: 1.9783 - categorical_accuracy: 0.1749 - val_loss: 2.0300 - val_categorical_accuracy: 0.1887 - lr: 0.0010\n",
      "Epoch 71/400\n",
      "47/47 [==============================] - 24s 502ms/step - loss: 1.9714 - categorical_accuracy: 0.1833 - val_loss: 1.9722 - val_categorical_accuracy: 0.2017 - lr: 0.0010\n",
      "Epoch 72/400\n",
      "47/47 [==============================] - 23s 499ms/step - loss: 1.9529 - categorical_accuracy: 0.1916 - val_loss: 1.9495 - val_categorical_accuracy: 0.2039 - lr: 0.0010\n",
      "Epoch 73/400\n",
      "47/47 [==============================] - 23s 497ms/step - loss: 1.9388 - categorical_accuracy: 0.1916 - val_loss: 1.9236 - val_categorical_accuracy: 0.2061 - lr: 0.0010\n",
      "Epoch 74/400\n",
      "47/47 [==============================] - 23s 489ms/step - loss: 1.9180 - categorical_accuracy: 0.2177 - val_loss: 1.9141 - val_categorical_accuracy: 0.2061 - lr: 0.0010\n",
      "Epoch 75/400\n",
      "47/47 [==============================] - 22s 477ms/step - loss: 1.9020 - categorical_accuracy: 0.2223 - val_loss: 1.9057 - val_categorical_accuracy: 0.2126 - lr: 0.0010\n",
      "Epoch 76/400\n",
      "47/47 [==============================] - 22s 478ms/step - loss: 1.8848 - categorical_accuracy: 0.2363 - val_loss: 1.9129 - val_categorical_accuracy: 0.2278 - lr: 0.0010\n",
      "Epoch 77/400\n",
      "47/47 [==============================] - 23s 482ms/step - loss: 1.9039 - categorical_accuracy: 0.2353 - val_loss: 1.8933 - val_categorical_accuracy: 0.2191 - lr: 0.0010\n",
      "Epoch 78/400\n",
      "47/47 [==============================] - 22s 478ms/step - loss: 1.8860 - categorical_accuracy: 0.2214 - val_loss: 1.9313 - val_categorical_accuracy: 0.2039 - lr: 0.0010\n",
      "Epoch 79/400\n",
      "47/47 [==============================] - 22s 478ms/step - loss: 1.8598 - categorical_accuracy: 0.2428 - val_loss: 1.9042 - val_categorical_accuracy: 0.2191 - lr: 0.0010\n",
      "Epoch 80/400\n",
      "47/47 [==============================] - 22s 478ms/step - loss: 1.8725 - categorical_accuracy: 0.2363 - val_loss: 1.9237 - val_categorical_accuracy: 0.2126 - lr: 0.0010\n",
      "Epoch 81/400\n",
      "47/47 [==============================] - 24s 502ms/step - loss: 1.8497 - categorical_accuracy: 0.2214 - val_loss: 1.9138 - val_categorical_accuracy: 0.2039 - lr: 0.0010\n",
      "Epoch 82/400\n",
      "47/47 [==============================] - 24s 505ms/step - loss: 1.8400 - categorical_accuracy: 0.2586 - val_loss: 1.9125 - val_categorical_accuracy: 0.2256 - lr: 0.0010\n",
      "Epoch 83/400\n",
      "47/47 [==============================] - 24s 502ms/step - loss: 1.8427 - categorical_accuracy: 0.2372 - val_loss: 1.8751 - val_categorical_accuracy: 0.2278 - lr: 0.0010\n",
      "Epoch 84/400\n",
      "47/47 [==============================] - 24s 509ms/step - loss: 1.8297 - categorical_accuracy: 0.2456 - val_loss: 1.8763 - val_categorical_accuracy: 0.2299 - lr: 0.0010\n",
      "Epoch 85/400\n",
      "47/47 [==============================] - 23s 489ms/step - loss: 1.8193 - categorical_accuracy: 0.2186 - val_loss: 1.8759 - val_categorical_accuracy: 0.2299 - lr: 0.0010\n",
      "Epoch 86/400\n",
      "47/47 [==============================] - 23s 488ms/step - loss: 1.7996 - categorical_accuracy: 0.2363 - val_loss: 1.9077 - val_categorical_accuracy: 0.2191 - lr: 0.0010\n",
      "Epoch 87/400\n",
      "47/47 [==============================] - 23s 494ms/step - loss: 1.7931 - categorical_accuracy: 0.2493 - val_loss: 1.9075 - val_categorical_accuracy: 0.2321 - lr: 0.0010\n",
      "Epoch 88/400\n",
      "47/47 [==============================] - 23s 490ms/step - loss: 1.7878 - categorical_accuracy: 0.2567 - val_loss: 1.8577 - val_categorical_accuracy: 0.2495 - lr: 0.0010\n",
      "Epoch 89/400\n",
      "47/47 [==============================] - 23s 500ms/step - loss: 1.7387 - categorical_accuracy: 0.2828 - val_loss: 1.8860 - val_categorical_accuracy: 0.2581 - lr: 0.0010\n",
      "Epoch 90/400\n",
      "47/47 [==============================] - 23s 495ms/step - loss: 1.7373 - categorical_accuracy: 0.2688 - val_loss: 1.9154 - val_categorical_accuracy: 0.2495 - lr: 0.0010\n",
      "Epoch 91/400\n",
      "47/47 [==============================] - 23s 487ms/step - loss: 1.7389 - categorical_accuracy: 0.2744 - val_loss: 1.8453 - val_categorical_accuracy: 0.2733 - lr: 0.0010\n",
      "Epoch 92/400\n",
      "47/47 [==============================] - 23s 486ms/step - loss: 1.7398 - categorical_accuracy: 0.2781 - val_loss: 1.8513 - val_categorical_accuracy: 0.2473 - lr: 0.0010\n",
      "Epoch 93/400\n",
      "47/47 [==============================] - 23s 488ms/step - loss: 1.7291 - categorical_accuracy: 0.2512 - val_loss: 1.8347 - val_categorical_accuracy: 0.2430 - lr: 0.0010\n",
      "Epoch 94/400\n",
      "47/47 [==============================] - 23s 484ms/step - loss: 1.7101 - categorical_accuracy: 0.2884 - val_loss: 1.9151 - val_categorical_accuracy: 0.2560 - lr: 0.0010\n",
      "Epoch 95/400\n",
      "47/47 [==============================] - 23s 486ms/step - loss: 1.7343 - categorical_accuracy: 0.2809 - val_loss: 1.8672 - val_categorical_accuracy: 0.2169 - lr: 0.0010\n",
      "Epoch 96/400\n",
      "47/47 [==============================] - 23s 486ms/step - loss: 1.7138 - categorical_accuracy: 0.2512 - val_loss: 1.8535 - val_categorical_accuracy: 0.2408 - lr: 0.0010\n",
      "Epoch 97/400\n",
      "47/47 [==============================] - 22s 478ms/step - loss: 1.6905 - categorical_accuracy: 0.2688 - val_loss: 1.8887 - val_categorical_accuracy: 0.2646 - lr: 0.0010\n",
      "Epoch 98/400\n",
      "47/47 [==============================] - 22s 479ms/step - loss: 1.6811 - categorical_accuracy: 0.2921 - val_loss: 1.8720 - val_categorical_accuracy: 0.2516 - lr: 0.0010\n",
      "Epoch 99/400\n",
      "47/47 [==============================] - 22s 478ms/step - loss: 1.6771 - categorical_accuracy: 0.3153 - val_loss: 1.8762 - val_categorical_accuracy: 0.2343 - lr: 0.0010\n",
      "Epoch 100/400\n",
      "47/47 [==============================] - 22s 479ms/step - loss: 1.6959 - categorical_accuracy: 0.2791 - val_loss: 1.8983 - val_categorical_accuracy: 0.2408 - lr: 0.0010\n",
      "Epoch 101/400\n",
      "47/47 [==============================] - 22s 480ms/step - loss: 1.6497 - categorical_accuracy: 0.2893 - val_loss: 1.8600 - val_categorical_accuracy: 0.2885 - lr: 0.0010\n",
      "Epoch 102/400\n",
      "47/47 [==============================] - 23s 485ms/step - loss: 1.6516 - categorical_accuracy: 0.3005 - val_loss: 1.8711 - val_categorical_accuracy: 0.2646 - lr: 0.0010\n",
      "Epoch 103/400\n",
      "47/47 [==============================] - 23s 481ms/step - loss: 1.6368 - categorical_accuracy: 0.3116 - val_loss: 1.9868 - val_categorical_accuracy: 0.2451 - lr: 0.0010\n",
      "Epoch 104/400\n",
      "47/47 [==============================] - 22s 476ms/step - loss: 1.6602 - categorical_accuracy: 0.2828 - val_loss: 1.8335 - val_categorical_accuracy: 0.2690 - lr: 0.0010\n",
      "Epoch 105/400\n",
      "47/47 [==============================] - 22s 475ms/step - loss: 1.6194 - categorical_accuracy: 0.2940 - val_loss: 1.8663 - val_categorical_accuracy: 0.2733 - lr: 0.0010\n",
      "Epoch 106/400\n",
      "47/47 [==============================] - 22s 476ms/step - loss: 1.6136 - categorical_accuracy: 0.3181 - val_loss: 1.8545 - val_categorical_accuracy: 0.2798 - lr: 0.0010\n",
      "Epoch 107/400\n",
      "47/47 [==============================] - 22s 475ms/step - loss: 1.6422 - categorical_accuracy: 0.2800 - val_loss: 1.8922 - val_categorical_accuracy: 0.2430 - lr: 0.0010\n",
      "Epoch 108/400\n",
      "47/47 [==============================] - 22s 475ms/step - loss: 1.6055 - categorical_accuracy: 0.3181 - val_loss: 1.7968 - val_categorical_accuracy: 0.2733 - lr: 0.0010\n",
      "Epoch 109/400\n",
      "47/47 [==============================] - 22s 475ms/step - loss: 1.6139 - categorical_accuracy: 0.3302 - val_loss: 1.8629 - val_categorical_accuracy: 0.2430 - lr: 0.0010\n",
      "Epoch 110/400\n",
      "47/47 [==============================] - 22s 474ms/step - loss: 1.5929 - categorical_accuracy: 0.3163 - val_loss: 1.8143 - val_categorical_accuracy: 0.2690 - lr: 0.0010\n",
      "Epoch 111/400\n",
      "47/47 [==============================] - 22s 475ms/step - loss: 1.5773 - categorical_accuracy: 0.3312 - val_loss: 1.8049 - val_categorical_accuracy: 0.2863 - lr: 0.0010\n",
      "Epoch 112/400\n",
      "47/47 [==============================] - 22s 476ms/step - loss: 1.5379 - categorical_accuracy: 0.3423 - val_loss: 1.8382 - val_categorical_accuracy: 0.3145 - lr: 0.0010\n",
      "Epoch 113/400\n",
      "47/47 [==============================] - 22s 476ms/step - loss: 1.5198 - categorical_accuracy: 0.3535 - val_loss: 1.7410 - val_categorical_accuracy: 0.3275 - lr: 0.0010\n",
      "Epoch 114/400\n",
      "47/47 [==============================] - 22s 475ms/step - loss: 1.5097 - categorical_accuracy: 0.3572 - val_loss: 1.8298 - val_categorical_accuracy: 0.2646 - lr: 0.0010\n",
      "Epoch 115/400\n",
      "47/47 [==============================] - 22s 474ms/step - loss: 1.4838 - categorical_accuracy: 0.3805 - val_loss: 1.8250 - val_categorical_accuracy: 0.3124 - lr: 0.0010\n",
      "Epoch 116/400\n",
      "47/47 [==============================] - 22s 478ms/step - loss: 1.5177 - categorical_accuracy: 0.3581 - val_loss: 1.7412 - val_categorical_accuracy: 0.3232 - lr: 0.0010\n",
      "Epoch 117/400\n",
      "47/47 [==============================] - 22s 477ms/step - loss: 1.4839 - categorical_accuracy: 0.3740 - val_loss: 1.7922 - val_categorical_accuracy: 0.3579 - lr: 0.0010\n",
      "Epoch 118/400\n",
      "47/47 [==============================] - 23s 484ms/step - loss: 1.5060 - categorical_accuracy: 0.3777 - val_loss: 1.6569 - val_categorical_accuracy: 0.3709 - lr: 0.0010\n",
      "Epoch 119/400\n",
      "47/47 [==============================] - 22s 474ms/step - loss: 1.4571 - categorical_accuracy: 0.3981 - val_loss: 1.7719 - val_categorical_accuracy: 0.3015 - lr: 0.0010\n",
      "Epoch 120/400\n",
      "47/47 [==============================] - 22s 474ms/step - loss: 1.4577 - categorical_accuracy: 0.3805 - val_loss: 1.6501 - val_categorical_accuracy: 0.3818 - lr: 0.0010\n",
      "Epoch 121/400\n",
      "47/47 [==============================] - 23s 483ms/step - loss: 1.4538 - categorical_accuracy: 0.3963 - val_loss: 1.7082 - val_categorical_accuracy: 0.3167 - lr: 0.0010\n",
      "Epoch 122/400\n",
      "47/47 [==============================] - 22s 479ms/step - loss: 1.3850 - categorical_accuracy: 0.4242 - val_loss: 1.7165 - val_categorical_accuracy: 0.3297 - lr: 0.0010\n",
      "Epoch 123/400\n",
      "47/47 [==============================] - 22s 475ms/step - loss: 1.3997 - categorical_accuracy: 0.3981 - val_loss: 1.8373 - val_categorical_accuracy: 0.2972 - lr: 0.0010\n",
      "Epoch 124/400\n",
      "47/47 [==============================] - 22s 474ms/step - loss: 1.4285 - categorical_accuracy: 0.4056 - val_loss: 1.6685 - val_categorical_accuracy: 0.3666 - lr: 0.0010\n",
      "Epoch 125/400\n",
      "47/47 [==============================] - 22s 474ms/step - loss: 1.3532 - categorical_accuracy: 0.4093 - val_loss: 1.6741 - val_categorical_accuracy: 0.3731 - lr: 0.0010\n",
      "Epoch 126/400\n",
      "47/47 [==============================] - 22s 476ms/step - loss: 1.3438 - categorical_accuracy: 0.4353 - val_loss: 1.7106 - val_categorical_accuracy: 0.3536 - lr: 0.0010\n",
      "Epoch 127/400\n",
      "47/47 [==============================] - 22s 475ms/step - loss: 1.3341 - categorical_accuracy: 0.4353 - val_loss: 1.7095 - val_categorical_accuracy: 0.3297 - lr: 0.0010\n",
      "Epoch 128/400\n",
      "47/47 [==============================] - 22s 476ms/step - loss: 1.3495 - categorical_accuracy: 0.4372 - val_loss: 1.7205 - val_categorical_accuracy: 0.3406 - lr: 0.0010\n",
      "Epoch 129/400\n",
      "47/47 [==============================] - 22s 476ms/step - loss: 1.3493 - categorical_accuracy: 0.4279 - val_loss: 1.6936 - val_categorical_accuracy: 0.3666 - lr: 0.0010\n",
      "Epoch 130/400\n",
      "47/47 [==============================] - 22s 477ms/step - loss: 1.3195 - categorical_accuracy: 0.4502 - val_loss: 1.6246 - val_categorical_accuracy: 0.3471 - lr: 0.0010\n",
      "Epoch 131/400\n",
      "47/47 [==============================] - 22s 477ms/step - loss: 1.3321 - categorical_accuracy: 0.4400 - val_loss: 1.6815 - val_categorical_accuracy: 0.3471 - lr: 0.0010\n",
      "Epoch 132/400\n",
      "47/47 [==============================] - 22s 478ms/step - loss: 1.2978 - categorical_accuracy: 0.4698 - val_loss: 1.6335 - val_categorical_accuracy: 0.4056 - lr: 0.0010\n",
      "Epoch 133/400\n",
      "47/47 [==============================] - 22s 476ms/step - loss: 1.2437 - categorical_accuracy: 0.4884 - val_loss: 1.6633 - val_categorical_accuracy: 0.3688 - lr: 0.0010\n",
      "Epoch 134/400\n",
      "47/47 [==============================] - 22s 475ms/step - loss: 1.2594 - categorical_accuracy: 0.4791 - val_loss: 1.6795 - val_categorical_accuracy: 0.3623 - lr: 0.0010\n",
      "Epoch 135/400\n",
      "47/47 [==============================] - 22s 478ms/step - loss: 1.2524 - categorical_accuracy: 0.4967 - val_loss: 1.6242 - val_categorical_accuracy: 0.3557 - lr: 0.0010\n",
      "Epoch 136/400\n",
      "47/47 [==============================] - 22s 476ms/step - loss: 1.2511 - categorical_accuracy: 0.4698 - val_loss: 1.7337 - val_categorical_accuracy: 0.3471 - lr: 0.0010\n",
      "Epoch 137/400\n",
      "47/47 [==============================] - 22s 475ms/step - loss: 1.2109 - categorical_accuracy: 0.4874 - val_loss: 1.5999 - val_categorical_accuracy: 0.3839 - lr: 0.0010\n",
      "Epoch 138/400\n",
      "47/47 [==============================] - 22s 473ms/step - loss: 1.1864 - categorical_accuracy: 0.5209 - val_loss: 1.6230 - val_categorical_accuracy: 0.3861 - lr: 0.0010\n",
      "Epoch 139/400\n",
      "47/47 [==============================] - 22s 476ms/step - loss: 1.2002 - categorical_accuracy: 0.5051 - val_loss: 1.6372 - val_categorical_accuracy: 0.4013 - lr: 0.0010\n",
      "Epoch 140/400\n",
      "47/47 [==============================] - 22s 478ms/step - loss: 1.1681 - categorical_accuracy: 0.5153 - val_loss: 1.6176 - val_categorical_accuracy: 0.3905 - lr: 0.0010\n",
      "Epoch 141/400\n",
      "47/47 [==============================] - 22s 478ms/step - loss: 1.2048 - categorical_accuracy: 0.5033 - val_loss: 1.6993 - val_categorical_accuracy: 0.3449 - lr: 0.0010\n",
      "Epoch 142/400\n",
      "47/47 [==============================] - 22s 477ms/step - loss: 1.1483 - categorical_accuracy: 0.5228 - val_loss: 1.5733 - val_categorical_accuracy: 0.4469 - lr: 0.0010\n",
      "Epoch 143/400\n",
      "47/47 [==============================] - 22s 478ms/step - loss: 1.1262 - categorical_accuracy: 0.5395 - val_loss: 1.6699 - val_categorical_accuracy: 0.3688 - lr: 0.0010\n",
      "Epoch 144/400\n",
      "47/47 [==============================] - 22s 480ms/step - loss: 1.1248 - categorical_accuracy: 0.5442 - val_loss: 1.6816 - val_categorical_accuracy: 0.3774 - lr: 0.0010\n",
      "Epoch 145/400\n",
      "47/47 [==============================] - 23s 482ms/step - loss: 1.0728 - categorical_accuracy: 0.5553 - val_loss: 1.6460 - val_categorical_accuracy: 0.4208 - lr: 0.0010\n",
      "Epoch 146/400\n",
      "47/47 [==============================] - 23s 481ms/step - loss: 1.0672 - categorical_accuracy: 0.5572 - val_loss: 1.6509 - val_categorical_accuracy: 0.4121 - lr: 0.0010\n",
      "Epoch 147/400\n",
      "47/47 [==============================] - 22s 479ms/step - loss: 1.0909 - categorical_accuracy: 0.5619 - val_loss: 1.6506 - val_categorical_accuracy: 0.3948 - lr: 0.0010\n",
      "Epoch 148/400\n",
      "47/47 [==============================] - 22s 479ms/step - loss: 1.0344 - categorical_accuracy: 0.6009 - val_loss: 1.6722 - val_categorical_accuracy: 0.4056 - lr: 0.0010\n",
      "Epoch 149/400\n",
      "47/47 [==============================] - 23s 481ms/step - loss: 1.3759 - categorical_accuracy: 0.4753 - val_loss: 1.7082 - val_categorical_accuracy: 0.3774 - lr: 0.0010\n",
      "Epoch 150/400\n",
      "47/47 [==============================] - 22s 480ms/step - loss: 1.0808 - categorical_accuracy: 0.5795 - val_loss: 1.8085 - val_categorical_accuracy: 0.3514 - lr: 0.0010\n",
      "Epoch 151/400\n",
      "47/47 [==============================] - 23s 483ms/step - loss: 1.0697 - categorical_accuracy: 0.5609 - val_loss: 1.7874 - val_categorical_accuracy: 0.3709 - lr: 0.0010\n",
      "Epoch 152/400\n",
      "47/47 [==============================] - 23s 488ms/step - loss: 1.0095 - categorical_accuracy: 0.6065 - val_loss: 1.6937 - val_categorical_accuracy: 0.4100 - lr: 0.0010\n",
      "Epoch 153/400\n",
      "47/47 [==============================] - 23s 482ms/step - loss: 0.9981 - categorical_accuracy: 0.6000 - val_loss: 1.7527 - val_categorical_accuracy: 0.3926 - lr: 0.0010\n",
      "Epoch 154/400\n",
      "47/47 [==============================] - 23s 484ms/step - loss: 1.0029 - categorical_accuracy: 0.5851 - val_loss: 1.6883 - val_categorical_accuracy: 0.4035 - lr: 0.0010\n",
      "Epoch 155/400\n",
      "47/47 [==============================] - 23s 482ms/step - loss: 1.0477 - categorical_accuracy: 0.5860 - val_loss: 1.6372 - val_categorical_accuracy: 0.4338 - lr: 0.0010\n",
      "Epoch 156/400\n",
      "47/47 [==============================] - 23s 486ms/step - loss: 0.9843 - categorical_accuracy: 0.5907 - val_loss: 1.6346 - val_categorical_accuracy: 0.4078 - lr: 0.0010\n",
      "Epoch 157/400\n",
      "47/47 [==============================] - 23s 485ms/step - loss: 0.9481 - categorical_accuracy: 0.6251 - val_loss: 1.6965 - val_categorical_accuracy: 0.4121 - lr: 0.0010\n",
      "Epoch 158/400\n",
      "47/47 [==============================] - 23s 483ms/step - loss: 0.9335 - categorical_accuracy: 0.6298 - val_loss: 1.6115 - val_categorical_accuracy: 0.4317 - lr: 0.0010\n",
      "Epoch 159/400\n",
      "47/47 [==============================] - 23s 481ms/step - loss: 0.8949 - categorical_accuracy: 0.6363 - val_loss: 1.8510 - val_categorical_accuracy: 0.3991 - lr: 0.0010\n",
      "Epoch 160/400\n",
      "47/47 [==============================] - 23s 482ms/step - loss: 0.8977 - categorical_accuracy: 0.6298 - val_loss: 1.6215 - val_categorical_accuracy: 0.4317 - lr: 0.0010\n",
      "Epoch 161/400\n",
      "47/47 [==============================] - 23s 482ms/step - loss: 0.8395 - categorical_accuracy: 0.6707 - val_loss: 1.7027 - val_categorical_accuracy: 0.4273 - lr: 0.0010\n",
      "Epoch 162/400\n",
      "47/47 [==============================] - 23s 482ms/step - loss: 0.8280 - categorical_accuracy: 0.6716 - val_loss: 1.6870 - val_categorical_accuracy: 0.4469 - lr: 0.0010\n",
      "Epoch 163/400\n",
      "47/47 [==============================] - 23s 484ms/step - loss: 0.8627 - categorical_accuracy: 0.6558 - val_loss: 1.7796 - val_categorical_accuracy: 0.4187 - lr: 0.0010\n",
      "Epoch 164/400\n",
      "47/47 [==============================] - 23s 482ms/step - loss: 0.9197 - categorical_accuracy: 0.6428 - val_loss: 1.8806 - val_categorical_accuracy: 0.3623 - lr: 0.0010\n",
      "Epoch 165/400\n",
      "47/47 [==============================] - 23s 482ms/step - loss: 0.8666 - categorical_accuracy: 0.6391 - val_loss: 1.7168 - val_categorical_accuracy: 0.4208 - lr: 0.0010\n",
      "Epoch 166/400\n",
      "47/47 [==============================] - 23s 483ms/step - loss: 0.8239 - categorical_accuracy: 0.6623 - val_loss: 1.6472 - val_categorical_accuracy: 0.4469 - lr: 0.0010\n",
      "Epoch 167/400\n",
      "47/47 [==============================] - 23s 486ms/step - loss: 0.8362 - categorical_accuracy: 0.6800 - val_loss: 1.6358 - val_categorical_accuracy: 0.4685 - lr: 0.0010\n",
      "Epoch 168/400\n",
      "47/47 [==============================] - 23s 485ms/step - loss: 0.7692 - categorical_accuracy: 0.6940 - val_loss: 1.7512 - val_categorical_accuracy: 0.4338 - lr: 0.0010\n",
      "Epoch 169/400\n",
      "47/47 [==============================] - 23s 482ms/step - loss: 0.7488 - categorical_accuracy: 0.6902 - val_loss: 1.7200 - val_categorical_accuracy: 0.4338 - lr: 0.0010\n",
      "Epoch 170/400\n",
      "47/47 [==============================] - 23s 486ms/step - loss: 0.7413 - categorical_accuracy: 0.7042 - val_loss: 1.7793 - val_categorical_accuracy: 0.4338 - lr: 0.0010\n",
      "Epoch 171/400\n",
      "47/47 [==============================] - 23s 484ms/step - loss: 0.7501 - categorical_accuracy: 0.6949 - val_loss: 1.8573 - val_categorical_accuracy: 0.4403 - lr: 0.0010\n",
      "Epoch 172/400\n",
      "47/47 [==============================] - 23s 487ms/step - loss: 0.7091 - categorical_accuracy: 0.7191 - val_loss: 1.8364 - val_categorical_accuracy: 0.4317 - lr: 0.0010\n",
      "Epoch 173/400\n",
      "47/47 [==============================] - 23s 484ms/step - loss: 0.7474 - categorical_accuracy: 0.7051 - val_loss: 1.7502 - val_categorical_accuracy: 0.4642 - lr: 0.0010\n",
      "Epoch 174/400\n",
      "47/47 [==============================] - 23s 483ms/step - loss: 0.7522 - categorical_accuracy: 0.7181 - val_loss: 1.8727 - val_categorical_accuracy: 0.4382 - lr: 0.0010\n",
      "Epoch 175/400\n",
      "47/47 [==============================] - 23s 487ms/step - loss: 0.7566 - categorical_accuracy: 0.7023 - val_loss: 1.8306 - val_categorical_accuracy: 0.4360 - lr: 0.0010\n",
      "Epoch 176/400\n",
      "47/47 [==============================] - 23s 486ms/step - loss: 0.6981 - categorical_accuracy: 0.7172 - val_loss: 2.0293 - val_categorical_accuracy: 0.3948 - lr: 0.0010\n",
      "Epoch 177/400\n",
      "47/47 [==============================] - 23s 486ms/step - loss: 0.7057 - categorical_accuracy: 0.7172 - val_loss: 1.7246 - val_categorical_accuracy: 0.4751 - lr: 0.0010\n",
      "Epoch 178/400\n",
      "47/47 [==============================] - 23s 483ms/step - loss: 0.6717 - categorical_accuracy: 0.7442 - val_loss: 1.7906 - val_categorical_accuracy: 0.4555 - lr: 0.0010\n",
      "Epoch 179/400\n",
      "47/47 [==============================] - 23s 482ms/step - loss: 0.6608 - categorical_accuracy: 0.7414 - val_loss: 1.8352 - val_categorical_accuracy: 0.4447 - lr: 0.0010\n",
      "Epoch 180/400\n",
      "47/47 [==============================] - 23s 483ms/step - loss: 0.6607 - categorical_accuracy: 0.7451 - val_loss: 1.8425 - val_categorical_accuracy: 0.4577 - lr: 0.0010\n",
      "Epoch 181/400\n",
      "47/47 [==============================] - 23s 485ms/step - loss: 0.6430 - categorical_accuracy: 0.7367 - val_loss: 1.8108 - val_categorical_accuracy: 0.4620 - lr: 0.0010\n",
      "Epoch 182/400\n",
      "47/47 [==============================] - 23s 485ms/step - loss: 0.6213 - categorical_accuracy: 0.7479 - val_loss: 1.9134 - val_categorical_accuracy: 0.4490 - lr: 0.0010\n",
      "Epoch 183/400\n",
      "47/47 [==============================] - 23s 484ms/step - loss: 0.6479 - categorical_accuracy: 0.7609 - val_loss: 1.7101 - val_categorical_accuracy: 0.5054 - lr: 0.0010\n",
      "Epoch 184/400\n",
      "47/47 [==============================] - 23s 483ms/step - loss: 0.6171 - categorical_accuracy: 0.7898 - val_loss: 1.9406 - val_categorical_accuracy: 0.4707 - lr: 0.0010\n",
      "Epoch 185/400\n",
      "47/47 [==============================] - 23s 483ms/step - loss: 0.6665 - categorical_accuracy: 0.7526 - val_loss: 1.8975 - val_categorical_accuracy: 0.4230 - lr: 0.0010\n",
      "Epoch 186/400\n",
      "47/47 [==============================] - 23s 488ms/step - loss: 0.5799 - categorical_accuracy: 0.7860 - val_loss: 1.8430 - val_categorical_accuracy: 0.4534 - lr: 0.0010\n",
      "Epoch 187/400\n",
      "47/47 [==============================] - 23s 488ms/step - loss: 0.5927 - categorical_accuracy: 0.7944 - val_loss: 1.7268 - val_categorical_accuracy: 0.5206 - lr: 0.0010\n",
      "Epoch 188/400\n",
      "47/47 [==============================] - 23s 488ms/step - loss: 0.5897 - categorical_accuracy: 0.7926 - val_loss: 1.9676 - val_categorical_accuracy: 0.4577 - lr: 0.0010\n",
      "Epoch 189/400\n",
      "47/47 [==============================] - 23s 483ms/step - loss: 0.5423 - categorical_accuracy: 0.8112 - val_loss: 1.7525 - val_categorical_accuracy: 0.5184 - lr: 0.0010\n",
      "Epoch 190/400\n",
      "47/47 [==============================] - 23s 486ms/step - loss: 0.5604 - categorical_accuracy: 0.8102 - val_loss: 2.0192 - val_categorical_accuracy: 0.4642 - lr: 0.0010\n",
      "Epoch 191/400\n",
      "47/47 [==============================] - 23s 485ms/step - loss: 0.5409 - categorical_accuracy: 0.8223 - val_loss: 1.9222 - val_categorical_accuracy: 0.4924 - lr: 0.0010\n",
      "Epoch 192/400\n",
      "47/47 [==============================] - 23s 484ms/step - loss: 0.5256 - categorical_accuracy: 0.8223 - val_loss: 1.9067 - val_categorical_accuracy: 0.4946 - lr: 0.0010\n",
      "Epoch 193/400\n",
      "47/47 [==============================] - 23s 484ms/step - loss: 0.5395 - categorical_accuracy: 0.8205 - val_loss: 1.8802 - val_categorical_accuracy: 0.4837 - lr: 0.0010\n",
      "Epoch 194/400\n",
      "47/47 [==============================] - 23s 488ms/step - loss: 0.5010 - categorical_accuracy: 0.8372 - val_loss: 1.9244 - val_categorical_accuracy: 0.4989 - lr: 0.0010\n",
      "Epoch 195/400\n",
      "47/47 [==============================] - 23s 485ms/step - loss: 0.4818 - categorical_accuracy: 0.8409 - val_loss: 2.0096 - val_categorical_accuracy: 0.5033 - lr: 0.0010\n",
      "Epoch 196/400\n",
      "47/47 [==============================] - 23s 488ms/step - loss: 0.5366 - categorical_accuracy: 0.8298 - val_loss: 1.8078 - val_categorical_accuracy: 0.4924 - lr: 0.0010\n",
      "Epoch 197/400\n",
      "47/47 [==============================] - 23s 490ms/step - loss: 0.5032 - categorical_accuracy: 0.8419 - val_loss: 1.9404 - val_categorical_accuracy: 0.5119 - lr: 0.0010\n",
      "Epoch 198/400\n",
      "47/47 [==============================] - 23s 485ms/step - loss: 0.4433 - categorical_accuracy: 0.8642 - val_loss: 1.9343 - val_categorical_accuracy: 0.5098 - lr: 0.0010\n",
      "Epoch 199/400\n",
      "47/47 [==============================] - 23s 489ms/step - loss: 0.4851 - categorical_accuracy: 0.8400 - val_loss: 1.9061 - val_categorical_accuracy: 0.5141 - lr: 0.0010\n",
      "Epoch 200/400\n",
      "47/47 [==============================] - 23s 485ms/step - loss: 0.5130 - categorical_accuracy: 0.8307 - val_loss: 1.8929 - val_categorical_accuracy: 0.4902 - lr: 0.0010\n",
      "Epoch 201/400\n",
      "47/47 [==============================] - 23s 484ms/step - loss: 0.3783 - categorical_accuracy: 0.8874 - val_loss: 2.0189 - val_categorical_accuracy: 0.4859 - lr: 0.0010\n",
      "Epoch 202/400\n",
      "47/47 [==============================] - 23s 484ms/step - loss: 0.4258 - categorical_accuracy: 0.8707 - val_loss: 2.0215 - val_categorical_accuracy: 0.4881 - lr: 0.0010\n",
      "Epoch 203/400\n",
      "47/47 [==============================] - 23s 485ms/step - loss: 0.4310 - categorical_accuracy: 0.8735 - val_loss: 2.0495 - val_categorical_accuracy: 0.5119 - lr: 0.0010\n",
      "Epoch 204/400\n",
      "47/47 [==============================] - 23s 488ms/step - loss: 0.4215 - categorical_accuracy: 0.8698 - val_loss: 1.9198 - val_categorical_accuracy: 0.5119 - lr: 0.0010\n",
      "Epoch 205/400\n",
      "47/47 [==============================] - 23s 485ms/step - loss: 0.4441 - categorical_accuracy: 0.8698 - val_loss: 2.0037 - val_categorical_accuracy: 0.5011 - lr: 0.0010\n",
      "Epoch 206/400\n",
      "47/47 [==============================] - 23s 484ms/step - loss: 0.3663 - categorical_accuracy: 0.8940 - val_loss: 1.9581 - val_categorical_accuracy: 0.5293 - lr: 0.0010\n",
      "Epoch 207/400\n",
      "47/47 [==============================] - 23s 483ms/step - loss: 0.4042 - categorical_accuracy: 0.8819 - val_loss: 1.9913 - val_categorical_accuracy: 0.5033 - lr: 0.0010\n",
      "Epoch 208/400\n",
      "47/47 [==============================] - 23s 485ms/step - loss: 0.4227 - categorical_accuracy: 0.8800 - val_loss: 2.1268 - val_categorical_accuracy: 0.4729 - lr: 0.0010\n",
      "Epoch 209/400\n",
      "47/47 [==============================] - 23s 484ms/step - loss: 0.4298 - categorical_accuracy: 0.8633 - val_loss: 1.8456 - val_categorical_accuracy: 0.5271 - lr: 0.0010\n",
      "Epoch 210/400\n",
      "47/47 [==============================] - 23s 488ms/step - loss: 0.3814 - categorical_accuracy: 0.8856 - val_loss: 1.9890 - val_categorical_accuracy: 0.5163 - lr: 0.0010\n",
      "Epoch 211/400\n",
      "47/47 [==============================] - 23s 483ms/step - loss: 0.3532 - categorical_accuracy: 0.8921 - val_loss: 2.0672 - val_categorical_accuracy: 0.4729 - lr: 0.0010\n",
      "Epoch 212/400\n",
      "47/47 [==============================] - 23s 489ms/step - loss: 0.3450 - categorical_accuracy: 0.8940 - val_loss: 2.1734 - val_categorical_accuracy: 0.4924 - lr: 0.0010\n",
      "Epoch 213/400\n",
      "47/47 [==============================] - 23s 483ms/step - loss: 0.3477 - categorical_accuracy: 0.8930 - val_loss: 2.1826 - val_categorical_accuracy: 0.4512 - lr: 0.0010\n",
      "Epoch 214/400\n",
      "47/47 [==============================] - 23s 485ms/step - loss: 0.3569 - categorical_accuracy: 0.8893 - val_loss: 1.9366 - val_categorical_accuracy: 0.5076 - lr: 0.0010\n",
      "Epoch 215/400\n",
      "47/47 [==============================] - 23s 484ms/step - loss: 0.3075 - categorical_accuracy: 0.9098 - val_loss: 1.9644 - val_categorical_accuracy: 0.5141 - lr: 0.0010\n",
      "Epoch 216/400\n",
      "47/47 [==============================] - 23s 484ms/step - loss: 0.3774 - categorical_accuracy: 0.8809 - val_loss: 2.0145 - val_categorical_accuracy: 0.4967 - lr: 0.0010\n",
      "Epoch 217/400\n",
      "47/47 [==============================] - 23s 488ms/step - loss: 0.3611 - categorical_accuracy: 0.8930 - val_loss: 2.0061 - val_categorical_accuracy: 0.5249 - lr: 0.0010\n",
      "Epoch 218/400\n",
      "47/47 [==============================] - 23s 490ms/step - loss: 0.2868 - categorical_accuracy: 0.9135 - val_loss: 2.1899 - val_categorical_accuracy: 0.5076 - lr: 0.0010\n",
      "Epoch 219/400\n",
      "47/47 [==============================] - 23s 483ms/step - loss: 0.2988 - categorical_accuracy: 0.9088 - val_loss: 2.0958 - val_categorical_accuracy: 0.4837 - lr: 0.0010\n",
      "Epoch 220/400\n",
      "47/47 [==============================] - 23s 484ms/step - loss: 0.3259 - categorical_accuracy: 0.9014 - val_loss: 2.0774 - val_categorical_accuracy: 0.4924 - lr: 0.0010\n",
      "Epoch 221/400\n",
      "47/47 [==============================] - 23s 485ms/step - loss: 0.3020 - categorical_accuracy: 0.9144 - val_loss: 2.2283 - val_categorical_accuracy: 0.4685 - lr: 0.0010\n",
      "Epoch 222/400\n",
      "47/47 [==============================] - 23s 485ms/step - loss: 0.2680 - categorical_accuracy: 0.9284 - val_loss: 2.4891 - val_categorical_accuracy: 0.4425 - lr: 0.0010\n",
      "Epoch 223/400\n",
      "47/47 [==============================] - 23s 485ms/step - loss: 0.2819 - categorical_accuracy: 0.9247 - val_loss: 1.9984 - val_categorical_accuracy: 0.5293 - lr: 0.0010\n",
      "Epoch 224/400\n",
      "47/47 [==============================] - 23s 484ms/step - loss: 0.2534 - categorical_accuracy: 0.9247 - val_loss: 2.3948 - val_categorical_accuracy: 0.5293 - lr: 0.0010\n",
      "Epoch 225/400\n",
      "47/47 [==============================] - 23s 485ms/step - loss: 0.2827 - categorical_accuracy: 0.9163 - val_loss: 2.1567 - val_categorical_accuracy: 0.5206 - lr: 0.0010\n",
      "Epoch 226/400\n",
      "47/47 [==============================] - 23s 484ms/step - loss: 0.2662 - categorical_accuracy: 0.9181 - val_loss: 1.9861 - val_categorical_accuracy: 0.5488 - lr: 0.0010\n",
      "Epoch 227/400\n",
      "47/47 [==============================] - 23s 483ms/step - loss: 0.2282 - categorical_accuracy: 0.9312 - val_loss: 2.2690 - val_categorical_accuracy: 0.5163 - lr: 0.0010\n",
      "Epoch 228/400\n",
      "47/47 [==============================] - 23s 483ms/step - loss: 0.2463 - categorical_accuracy: 0.9340 - val_loss: 2.2623 - val_categorical_accuracy: 0.5054 - lr: 0.0010\n",
      "Epoch 229/400\n",
      "47/47 [==============================] - 23s 483ms/step - loss: 0.2989 - categorical_accuracy: 0.9051 - val_loss: 2.0695 - val_categorical_accuracy: 0.5293 - lr: 0.0010\n",
      "Epoch 230/400\n",
      "47/47 [==============================] - 23s 485ms/step - loss: 0.2616 - categorical_accuracy: 0.9200 - val_loss: 2.1667 - val_categorical_accuracy: 0.4924 - lr: 0.0010\n",
      "Epoch 231/400\n",
      "47/47 [==============================] - 23s 484ms/step - loss: 0.2362 - categorical_accuracy: 0.9330 - val_loss: 2.2074 - val_categorical_accuracy: 0.5119 - lr: 0.0010\n",
      "Epoch 232/400\n",
      "47/47 [==============================] - 23s 483ms/step - loss: 0.2510 - categorical_accuracy: 0.9274 - val_loss: 2.1063 - val_categorical_accuracy: 0.5358 - lr: 0.0010\n",
      "Epoch 233/400\n",
      "47/47 [==============================] - 23s 483ms/step - loss: 0.2401 - categorical_accuracy: 0.9293 - val_loss: 1.9912 - val_categorical_accuracy: 0.5575 - lr: 0.0010\n",
      "Epoch 234/400\n",
      "47/47 [==============================] - 23s 486ms/step - loss: 0.2556 - categorical_accuracy: 0.9312 - val_loss: 2.1702 - val_categorical_accuracy: 0.5358 - lr: 0.0010\n",
      "Epoch 235/400\n",
      "47/47 [==============================] - 23s 484ms/step - loss: 0.2874 - categorical_accuracy: 0.9163 - val_loss: 2.2653 - val_categorical_accuracy: 0.5011 - lr: 0.0010\n",
      "Epoch 236/400\n",
      "47/47 [==============================] - 23s 486ms/step - loss: 0.3239 - categorical_accuracy: 0.9107 - val_loss: 2.1574 - val_categorical_accuracy: 0.5184 - lr: 0.0010\n",
      "Epoch 237/400\n",
      "47/47 [==============================] - 20s 433ms/step - loss: 0.2084 - categorical_accuracy: 0.9414 - val_loss: 2.0613 - val_categorical_accuracy: 0.5401 - lr: 0.0010\n",
      "Epoch 238/400\n",
      "47/47 [==============================] - 19s 397ms/step - loss: 0.2709 - categorical_accuracy: 0.9237 - val_loss: 2.2073 - val_categorical_accuracy: 0.5184 - lr: 0.0010\n",
      "Epoch 239/400\n",
      "47/47 [==============================] - 19s 396ms/step - loss: 0.2111 - categorical_accuracy: 0.9414 - val_loss: 2.2477 - val_categorical_accuracy: 0.5184 - lr: 0.0010\n",
      "Epoch 240/400\n",
      "47/47 [==============================] - 19s 395ms/step - loss: 0.2108 - categorical_accuracy: 0.9498 - val_loss: 2.2562 - val_categorical_accuracy: 0.5141 - lr: 0.0010\n",
      "Epoch 241/400\n",
      "47/47 [==============================] - 19s 396ms/step - loss: 0.2289 - categorical_accuracy: 0.9395 - val_loss: 2.3188 - val_categorical_accuracy: 0.5228 - lr: 0.0010\n",
      "Epoch 242/400\n",
      "47/47 [==============================] - 19s 396ms/step - loss: 0.2183 - categorical_accuracy: 0.9423 - val_loss: 2.3278 - val_categorical_accuracy: 0.5380 - lr: 0.0010\n",
      "Epoch 243/400\n",
      "47/47 [==============================] - 19s 396ms/step - loss: 0.2227 - categorical_accuracy: 0.9405 - val_loss: 2.2009 - val_categorical_accuracy: 0.5445 - lr: 0.0010\n",
      "Epoch 244/400\n",
      "47/47 [==============================] - 19s 397ms/step - loss: 0.2118 - categorical_accuracy: 0.9470 - val_loss: 2.3232 - val_categorical_accuracy: 0.5206 - lr: 0.0010\n",
      "Epoch 245/400\n",
      "47/47 [==============================] - 18s 394ms/step - loss: 0.3232 - categorical_accuracy: 0.9126 - val_loss: 2.2218 - val_categorical_accuracy: 0.5011 - lr: 0.0010\n",
      "Epoch 246/400\n",
      "47/47 [==============================] - 19s 396ms/step - loss: 0.1978 - categorical_accuracy: 0.9507 - val_loss: 2.3510 - val_categorical_accuracy: 0.5141 - lr: 0.0010\n",
      "Epoch 247/400\n",
      "47/47 [==============================] - 19s 399ms/step - loss: 0.2044 - categorical_accuracy: 0.9488 - val_loss: 2.2303 - val_categorical_accuracy: 0.5249 - lr: 0.0010\n",
      "Epoch 248/400\n",
      "47/47 [==============================] - 19s 395ms/step - loss: 0.2095 - categorical_accuracy: 0.9442 - val_loss: 2.2919 - val_categorical_accuracy: 0.5206 - lr: 0.0010\n",
      "Epoch 249/400\n",
      "47/47 [==============================] - 18s 394ms/step - loss: 0.1728 - categorical_accuracy: 0.9516 - val_loss: 2.4548 - val_categorical_accuracy: 0.5098 - lr: 0.0010\n",
      "Epoch 250/400\n",
      "47/47 [==============================] - 18s 394ms/step - loss: 0.1895 - categorical_accuracy: 0.9516 - val_loss: 2.3844 - val_categorical_accuracy: 0.5380 - lr: 0.0010\n",
      "Epoch 251/400\n",
      "47/47 [==============================] - 18s 394ms/step - loss: 0.2154 - categorical_accuracy: 0.9414 - val_loss: 2.4539 - val_categorical_accuracy: 0.5119 - lr: 0.0010\n",
      "Epoch 252/400\n",
      "47/47 [==============================] - 19s 397ms/step - loss: 0.2022 - categorical_accuracy: 0.9488 - val_loss: 2.3317 - val_categorical_accuracy: 0.5336 - lr: 0.0010\n",
      "Epoch 253/400\n",
      "47/47 [==============================] - 19s 397ms/step - loss: 0.2120 - categorical_accuracy: 0.9395 - val_loss: 2.2790 - val_categorical_accuracy: 0.5163 - lr: 0.0010\n",
      "Epoch 254/400\n",
      "47/47 [==============================] - 18s 393ms/step - loss: 0.1846 - categorical_accuracy: 0.9423 - val_loss: 2.4854 - val_categorical_accuracy: 0.4902 - lr: 0.0010\n",
      "Epoch 255/400\n",
      "47/47 [==============================] - 19s 396ms/step - loss: 0.1981 - categorical_accuracy: 0.9405 - val_loss: 2.3739 - val_categorical_accuracy: 0.5011 - lr: 0.0010\n",
      "Epoch 256/400\n",
      "47/47 [==============================] - 19s 395ms/step - loss: 0.1811 - categorical_accuracy: 0.9498 - val_loss: 2.3199 - val_categorical_accuracy: 0.5336 - lr: 0.0010\n",
      "Epoch 257/400\n",
      "47/47 [==============================] - 18s 394ms/step - loss: 0.2569 - categorical_accuracy: 0.9284 - val_loss: 2.3238 - val_categorical_accuracy: 0.5228 - lr: 0.0010\n",
      "Epoch 258/400\n",
      "47/47 [==============================] - 19s 396ms/step - loss: 0.1859 - categorical_accuracy: 0.9498 - val_loss: 2.3138 - val_categorical_accuracy: 0.5380 - lr: 0.0010\n",
      "Epoch 259/400\n",
      "47/47 [==============================] - 19s 397ms/step - loss: 0.1450 - categorical_accuracy: 0.9572 - val_loss: 2.4322 - val_categorical_accuracy: 0.5336 - lr: 0.0010\n",
      "Epoch 260/400\n",
      "47/47 [==============================] - 18s 393ms/step - loss: 0.1416 - categorical_accuracy: 0.9665 - val_loss: 2.6354 - val_categorical_accuracy: 0.5076 - lr: 0.0010\n",
      "Epoch 261/400\n",
      "47/47 [==============================] - 19s 396ms/step - loss: 0.1978 - categorical_accuracy: 0.9479 - val_loss: 2.3786 - val_categorical_accuracy: 0.5358 - lr: 0.0010\n",
      "Epoch 262/400\n",
      "47/47 [==============================] - 18s 391ms/step - loss: 0.2194 - categorical_accuracy: 0.9349 - val_loss: 2.2651 - val_categorical_accuracy: 0.5271 - lr: 0.0010\n",
      "Epoch 263/400\n",
      "47/47 [==============================] - 19s 395ms/step - loss: 0.1919 - categorical_accuracy: 0.9442 - val_loss: 2.1623 - val_categorical_accuracy: 0.5445 - lr: 0.0010\n",
      "Epoch 264/400\n",
      "47/47 [==============================] - 19s 395ms/step - loss: 0.1448 - categorical_accuracy: 0.9609 - val_loss: 2.3096 - val_categorical_accuracy: 0.5597 - lr: 0.0010\n",
      "Epoch 265/400\n",
      "47/47 [==============================] - 19s 395ms/step - loss: 0.1449 - categorical_accuracy: 0.9637 - val_loss: 2.3591 - val_categorical_accuracy: 0.5206 - lr: 0.0010\n",
      "Epoch 266/400\n",
      "47/47 [==============================] - 18s 393ms/step - loss: 0.1569 - categorical_accuracy: 0.9591 - val_loss: 2.2731 - val_categorical_accuracy: 0.5510 - lr: 0.0010\n",
      "Epoch 267/400\n",
      "47/47 [==============================] - 18s 394ms/step - loss: 0.1731 - categorical_accuracy: 0.9619 - val_loss: 2.3216 - val_categorical_accuracy: 0.5336 - lr: 0.0010\n",
      "Epoch 268/400\n",
      "47/47 [==============================] - 18s 394ms/step - loss: 0.2052 - categorical_accuracy: 0.9479 - val_loss: 2.4569 - val_categorical_accuracy: 0.5315 - lr: 0.0010\n",
      "Epoch 269/400\n",
      "47/47 [==============================] - 18s 393ms/step - loss: 0.2508 - categorical_accuracy: 0.9405 - val_loss: 2.3455 - val_categorical_accuracy: 0.5249 - lr: 0.0010\n",
      "Epoch 270/400\n",
      "47/47 [==============================] - 18s 393ms/step - loss: 0.1904 - categorical_accuracy: 0.9507 - val_loss: 2.4066 - val_categorical_accuracy: 0.5141 - lr: 0.0010\n",
      "Epoch 271/400\n",
      "47/47 [==============================] - 18s 395ms/step - loss: 0.1513 - categorical_accuracy: 0.9591 - val_loss: 2.4817 - val_categorical_accuracy: 0.5249 - lr: 0.0010\n",
      "Epoch 272/400\n",
      "47/47 [==============================] - 18s 393ms/step - loss: 0.2075 - categorical_accuracy: 0.9460 - val_loss: 2.4067 - val_categorical_accuracy: 0.5249 - lr: 0.0010\n",
      "Epoch 273/400\n",
      "47/47 [==============================] - 18s 392ms/step - loss: 0.1876 - categorical_accuracy: 0.9591 - val_loss: 2.3589 - val_categorical_accuracy: 0.5466 - lr: 0.0010\n",
      "Epoch 274/400\n",
      "47/47 [==============================] - 18s 393ms/step - loss: 0.1215 - categorical_accuracy: 0.9712 - val_loss: 2.4578 - val_categorical_accuracy: 0.5445 - lr: 0.0010\n",
      "Epoch 275/400\n",
      "47/47 [==============================] - 18s 394ms/step - loss: 0.1652 - categorical_accuracy: 0.9507 - val_loss: 2.4637 - val_categorical_accuracy: 0.5249 - lr: 0.0010\n",
      "Epoch 276/400\n",
      "47/47 [==============================] - 19s 396ms/step - loss: 0.1230 - categorical_accuracy: 0.9656 - val_loss: 2.5843 - val_categorical_accuracy: 0.5011 - lr: 0.0010\n",
      "Epoch 277/400\n",
      "47/47 [==============================] - 19s 401ms/step - loss: 0.1926 - categorical_accuracy: 0.9507 - val_loss: 2.5880 - val_categorical_accuracy: 0.4924 - lr: 0.0010\n",
      "Epoch 278/400\n",
      "47/47 [==============================] - 19s 414ms/step - loss: 0.1432 - categorical_accuracy: 0.9600 - val_loss: 2.5272 - val_categorical_accuracy: 0.5054 - lr: 0.0010\n",
      "Epoch 279/400\n",
      "47/47 [==============================] - 19s 396ms/step - loss: 0.1482 - categorical_accuracy: 0.9600 - val_loss: 2.4864 - val_categorical_accuracy: 0.5206 - lr: 0.0010\n",
      "Epoch 280/400\n",
      "47/47 [==============================] - 18s 394ms/step - loss: 0.1522 - categorical_accuracy: 0.9581 - val_loss: 2.4758 - val_categorical_accuracy: 0.5119 - lr: 0.0010\n",
      "Epoch 281/400\n",
      "47/47 [==============================] - 18s 394ms/step - loss: 0.1426 - categorical_accuracy: 0.9628 - val_loss: 2.6799 - val_categorical_accuracy: 0.4729 - lr: 0.0010\n",
      "Epoch 282/400\n",
      "47/47 [==============================] - 18s 392ms/step - loss: 0.1807 - categorical_accuracy: 0.9516 - val_loss: 2.4685 - val_categorical_accuracy: 0.5098 - lr: 0.0010\n",
      "Epoch 283/400\n",
      "47/47 [==============================] - 19s 397ms/step - loss: 0.1430 - categorical_accuracy: 0.9619 - val_loss: 2.3850 - val_categorical_accuracy: 0.5206 - lr: 0.0010\n",
      "Epoch 284/400\n",
      "47/47 [==============================] - 19s 395ms/step - loss: 0.1252 - categorical_accuracy: 0.9628 - val_loss: 2.6031 - val_categorical_accuracy: 0.5119 - lr: 0.0010\n",
      "Epoch 285/400\n",
      "47/47 [==============================] - 19s 396ms/step - loss: 0.1319 - categorical_accuracy: 0.9674 - val_loss: 2.5943 - val_categorical_accuracy: 0.5336 - lr: 0.0010\n",
      "Epoch 286/400\n",
      "47/47 [==============================] - 19s 395ms/step - loss: 0.1318 - categorical_accuracy: 0.9637 - val_loss: 2.4655 - val_categorical_accuracy: 0.5271 - lr: 0.0010\n",
      "Epoch 287/400\n",
      "47/47 [==============================] - 18s 394ms/step - loss: 0.1494 - categorical_accuracy: 0.9647 - val_loss: 2.4904 - val_categorical_accuracy: 0.5141 - lr: 0.0010\n",
      "Epoch 288/400\n",
      "47/47 [==============================] - 19s 396ms/step - loss: 0.1217 - categorical_accuracy: 0.9684 - val_loss: 2.4329 - val_categorical_accuracy: 0.5358 - lr: 0.0010\n",
      "Epoch 289/400\n",
      "47/47 [==============================] - 19s 401ms/step - loss: 0.1526 - categorical_accuracy: 0.9637 - val_loss: 2.4755 - val_categorical_accuracy: 0.5315 - lr: 0.0010\n",
      "Epoch 290/400\n",
      "47/47 [==============================] - 19s 396ms/step - loss: 0.1273 - categorical_accuracy: 0.9628 - val_loss: 2.5314 - val_categorical_accuracy: 0.5401 - lr: 0.0010\n",
      "Epoch 291/400\n",
      "47/47 [==============================] - 18s 394ms/step - loss: 0.1153 - categorical_accuracy: 0.9665 - val_loss: 2.5239 - val_categorical_accuracy: 0.5445 - lr: 0.0010\n",
      "Epoch 292/400\n",
      "47/47 [==============================] - 19s 396ms/step - loss: 0.1177 - categorical_accuracy: 0.9721 - val_loss: 2.5308 - val_categorical_accuracy: 0.5271 - lr: 0.0010\n",
      "Epoch 293/400\n",
      "47/47 [==============================] - 18s 393ms/step - loss: 0.1241 - categorical_accuracy: 0.9665 - val_loss: 2.4892 - val_categorical_accuracy: 0.5076 - lr: 0.0010\n",
      "Epoch 294/400\n",
      "47/47 [==============================] - 18s 394ms/step - loss: 0.1717 - categorical_accuracy: 0.9526 - val_loss: 2.6258 - val_categorical_accuracy: 0.5119 - lr: 0.0010\n",
      "Epoch 295/400\n",
      "47/47 [==============================] - 18s 393ms/step - loss: 0.1379 - categorical_accuracy: 0.9637 - val_loss: 2.6297 - val_categorical_accuracy: 0.5336 - lr: 0.0010\n",
      "Epoch 296/400\n",
      "47/47 [==============================] - 18s 393ms/step - loss: 0.0925 - categorical_accuracy: 0.9740 - val_loss: 2.5018 - val_categorical_accuracy: 0.5336 - lr: 0.0010\n",
      "Epoch 297/400\n",
      "47/47 [==============================] - 18s 394ms/step - loss: 0.1069 - categorical_accuracy: 0.9674 - val_loss: 2.6430 - val_categorical_accuracy: 0.5076 - lr: 0.0010\n",
      "Epoch 298/400\n",
      "47/47 [==============================] - 18s 392ms/step - loss: 0.1382 - categorical_accuracy: 0.9637 - val_loss: 2.5367 - val_categorical_accuracy: 0.5466 - lr: 0.0010\n",
      "Epoch 299/400\n",
      "47/47 [==============================] - 18s 393ms/step - loss: 0.0978 - categorical_accuracy: 0.9702 - val_loss: 2.5575 - val_categorical_accuracy: 0.5293 - lr: 0.0010\n",
      "Epoch 300/400\n",
      "47/47 [==============================] - 19s 395ms/step - loss: 0.1359 - categorical_accuracy: 0.9656 - val_loss: 2.6445 - val_categorical_accuracy: 0.5184 - lr: 0.0010\n",
      "Epoch 301/400\n",
      "47/47 [==============================] - 19s 395ms/step - loss: 0.1443 - categorical_accuracy: 0.9665 - val_loss: 2.4623 - val_categorical_accuracy: 0.5228 - lr: 0.0010\n",
      "Epoch 302/400\n",
      "47/47 [==============================] - 18s 392ms/step - loss: 0.0981 - categorical_accuracy: 0.9730 - val_loss: 2.6182 - val_categorical_accuracy: 0.5098 - lr: 0.0010\n",
      "Epoch 303/400\n",
      "47/47 [==============================] - 19s 401ms/step - loss: 0.1200 - categorical_accuracy: 0.9693 - val_loss: 2.4026 - val_categorical_accuracy: 0.5380 - lr: 0.0010\n",
      "Epoch 304/400\n",
      "47/47 [==============================] - 18s 394ms/step - loss: 0.0813 - categorical_accuracy: 0.9777 - val_loss: 2.6734 - val_categorical_accuracy: 0.5206 - lr: 0.0010\n",
      "Epoch 305/400\n",
      "47/47 [==============================] - 18s 394ms/step - loss: 0.1166 - categorical_accuracy: 0.9721 - val_loss: 2.6180 - val_categorical_accuracy: 0.5228 - lr: 0.0010\n",
      "Epoch 306/400\n",
      "47/47 [==============================] - 18s 395ms/step - loss: 0.0957 - categorical_accuracy: 0.9777 - val_loss: 2.5965 - val_categorical_accuracy: 0.5184 - lr: 0.0010\n",
      "Epoch 307/400\n",
      "47/47 [==============================] - 19s 396ms/step - loss: 0.0990 - categorical_accuracy: 0.9721 - val_loss: 2.6568 - val_categorical_accuracy: 0.5336 - lr: 0.0010\n",
      "Epoch 308/400\n",
      "47/47 [==============================] - 19s 395ms/step - loss: 0.1340 - categorical_accuracy: 0.9628 - val_loss: 2.6193 - val_categorical_accuracy: 0.5315 - lr: 0.0010\n",
      "Epoch 309/400\n",
      "47/47 [==============================] - 19s 410ms/step - loss: 0.0984 - categorical_accuracy: 0.9740 - val_loss: 2.7771 - val_categorical_accuracy: 0.5293 - lr: 0.0010\n",
      "Epoch 310/400\n",
      "47/47 [==============================] - 19s 403ms/step - loss: 0.0939 - categorical_accuracy: 0.9740 - val_loss: 2.6530 - val_categorical_accuracy: 0.5315 - lr: 0.0010\n",
      "Epoch 311/400\n",
      "47/47 [==============================] - 19s 409ms/step - loss: 0.0985 - categorical_accuracy: 0.9674 - val_loss: 2.7644 - val_categorical_accuracy: 0.5163 - lr: 0.0010\n",
      "Epoch 312/400\n",
      "47/47 [==============================] - 19s 409ms/step - loss: 0.0882 - categorical_accuracy: 0.9786 - val_loss: 2.8421 - val_categorical_accuracy: 0.4989 - lr: 0.0010\n",
      "Epoch 313/400\n",
      "47/47 [==============================] - 19s 405ms/step - loss: 0.1355 - categorical_accuracy: 0.9684 - val_loss: 2.7172 - val_categorical_accuracy: 0.5271 - lr: 0.0010\n",
      "Epoch 314/400\n",
      "47/47 [==============================] - 19s 396ms/step - loss: 0.0624 - categorical_accuracy: 0.9823 - val_loss: 2.8645 - val_categorical_accuracy: 0.5315 - lr: 0.0010\n",
      "Epoch 315/400\n",
      "47/47 [==============================] - 19s 402ms/step - loss: 0.1027 - categorical_accuracy: 0.9721 - val_loss: 2.8782 - val_categorical_accuracy: 0.5249 - lr: 0.0010\n",
      "Epoch 316/400\n",
      "47/47 [==============================] - 19s 403ms/step - loss: 0.1163 - categorical_accuracy: 0.9712 - val_loss: 2.8959 - val_categorical_accuracy: 0.5293 - lr: 0.0010\n",
      "Epoch 317/400\n",
      "47/47 [==============================] - 19s 401ms/step - loss: 0.0778 - categorical_accuracy: 0.9786 - val_loss: 2.5846 - val_categorical_accuracy: 0.5510 - lr: 0.0010\n",
      "Epoch 318/400\n",
      "47/47 [==============================] - 19s 397ms/step - loss: 0.1050 - categorical_accuracy: 0.9721 - val_loss: 2.6543 - val_categorical_accuracy: 0.5141 - lr: 0.0010\n",
      "Epoch 319/400\n",
      "47/47 [==============================] - 18s 394ms/step - loss: 0.0706 - categorical_accuracy: 0.9805 - val_loss: 2.8207 - val_categorical_accuracy: 0.5249 - lr: 0.0010\n",
      "Epoch 320/400\n",
      "47/47 [==============================] - 19s 394ms/step - loss: 0.1046 - categorical_accuracy: 0.9656 - val_loss: 2.6461 - val_categorical_accuracy: 0.5293 - lr: 0.0010\n",
      "Epoch 321/400\n",
      "47/47 [==============================] - 18s 394ms/step - loss: 0.0929 - categorical_accuracy: 0.9693 - val_loss: 2.6966 - val_categorical_accuracy: 0.5163 - lr: 0.0010\n",
      "Epoch 322/400\n",
      "47/47 [==============================] - 18s 393ms/step - loss: 0.0866 - categorical_accuracy: 0.9805 - val_loss: 2.7144 - val_categorical_accuracy: 0.5315 - lr: 0.0010\n",
      "Epoch 323/400\n",
      "47/47 [==============================] - 18s 393ms/step - loss: 0.1065 - categorical_accuracy: 0.9740 - val_loss: 2.8314 - val_categorical_accuracy: 0.5206 - lr: 0.0010\n",
      "Epoch 324/400\n",
      "47/47 [==============================] - 18s 393ms/step - loss: 0.0667 - categorical_accuracy: 0.9842 - val_loss: 2.7709 - val_categorical_accuracy: 0.5249 - lr: 0.0010\n",
      "Epoch 325/400\n",
      "47/47 [==============================] - 18s 391ms/step - loss: 0.1620 - categorical_accuracy: 0.9572 - val_loss: 2.4869 - val_categorical_accuracy: 0.5553 - lr: 0.0010\n",
      "Epoch 326/400\n",
      "47/47 [==============================] - 19s 395ms/step - loss: 0.1281 - categorical_accuracy: 0.9665 - val_loss: 2.4218 - val_categorical_accuracy: 0.5640 - lr: 0.0010\n",
      "Epoch 327/400\n",
      "47/47 [==============================] - 19s 395ms/step - loss: 0.1065 - categorical_accuracy: 0.9767 - val_loss: 2.5093 - val_categorical_accuracy: 0.5553 - lr: 0.0010\n",
      "Epoch 328/400\n",
      "47/47 [==============================] - 19s 395ms/step - loss: 0.0663 - categorical_accuracy: 0.9851 - val_loss: 2.7862 - val_categorical_accuracy: 0.5510 - lr: 0.0010\n",
      "Epoch 329/400\n",
      "47/47 [==============================] - 19s 397ms/step - loss: 0.1033 - categorical_accuracy: 0.9749 - val_loss: 2.6939 - val_categorical_accuracy: 0.5597 - lr: 0.0010\n",
      "Epoch 330/400\n",
      "47/47 [==============================] - 19s 400ms/step - loss: 0.0948 - categorical_accuracy: 0.9758 - val_loss: 2.6529 - val_categorical_accuracy: 0.5510 - lr: 0.0010\n",
      "Epoch 331/400\n",
      "47/47 [==============================] - 19s 401ms/step - loss: 0.1042 - categorical_accuracy: 0.9730 - val_loss: 2.9104 - val_categorical_accuracy: 0.5119 - lr: 0.0010\n",
      "Epoch 332/400\n",
      "47/47 [==============================] - 19s 395ms/step - loss: 0.1278 - categorical_accuracy: 0.9740 - val_loss: 2.5554 - val_categorical_accuracy: 0.5445 - lr: 0.0010\n",
      "Epoch 333/400\n",
      "47/47 [==============================] - 19s 396ms/step - loss: 0.0685 - categorical_accuracy: 0.9814 - val_loss: 2.5594 - val_categorical_accuracy: 0.5575 - lr: 0.0010\n",
      "Epoch 334/400\n",
      "47/47 [==============================] - 19s 398ms/step - loss: 0.0998 - categorical_accuracy: 0.9684 - val_loss: 2.6588 - val_categorical_accuracy: 0.5184 - lr: 0.0010\n",
      "Epoch 335/400\n",
      "47/47 [==============================] - 19s 397ms/step - loss: 0.0689 - categorical_accuracy: 0.9814 - val_loss: 2.7807 - val_categorical_accuracy: 0.5184 - lr: 0.0010\n",
      "Epoch 336/400\n",
      "47/47 [==============================] - 19s 397ms/step - loss: 0.0611 - categorical_accuracy: 0.9842 - val_loss: 2.7499 - val_categorical_accuracy: 0.5336 - lr: 0.0010\n",
      "Epoch 337/400\n",
      "47/47 [==============================] - 19s 397ms/step - loss: 0.0515 - categorical_accuracy: 0.9879 - val_loss: 3.0020 - val_categorical_accuracy: 0.5249 - lr: 0.0010\n",
      "Epoch 338/400\n",
      "47/47 [==============================] - 19s 396ms/step - loss: 0.1193 - categorical_accuracy: 0.9684 - val_loss: 2.7568 - val_categorical_accuracy: 0.5380 - lr: 0.0010\n",
      "Epoch 339/400\n",
      "47/47 [==============================] - 19s 397ms/step - loss: 0.0593 - categorical_accuracy: 0.9851 - val_loss: 2.8577 - val_categorical_accuracy: 0.5466 - lr: 0.0010\n",
      "Epoch 340/400\n",
      "47/47 [==============================] - 19s 398ms/step - loss: 0.0652 - categorical_accuracy: 0.9805 - val_loss: 2.7980 - val_categorical_accuracy: 0.5618 - lr: 0.0010\n",
      "Epoch 341/400\n",
      "47/47 [==============================] - 19s 396ms/step - loss: 0.0942 - categorical_accuracy: 0.9786 - val_loss: 2.8659 - val_categorical_accuracy: 0.5401 - lr: 0.0010\n",
      "Epoch 342/400\n",
      "47/47 [==============================] - 19s 396ms/step - loss: 0.0809 - categorical_accuracy: 0.9777 - val_loss: 3.0218 - val_categorical_accuracy: 0.5163 - lr: 0.0010\n",
      "Epoch 343/400\n",
      "47/47 [==============================] - 19s 395ms/step - loss: 0.1664 - categorical_accuracy: 0.9553 - val_loss: 2.8479 - val_categorical_accuracy: 0.5466 - lr: 0.0010\n",
      "Epoch 344/400\n",
      "47/47 [==============================] - 19s 395ms/step - loss: 0.0816 - categorical_accuracy: 0.9767 - val_loss: 2.7225 - val_categorical_accuracy: 0.5618 - lr: 0.0010\n",
      "Epoch 345/400\n",
      "47/47 [==============================] - 19s 397ms/step - loss: 0.0909 - categorical_accuracy: 0.9730 - val_loss: 2.8034 - val_categorical_accuracy: 0.5597 - lr: 0.0010\n",
      "Epoch 346/400\n",
      "47/47 [==============================] - 18s 394ms/step - loss: 0.0788 - categorical_accuracy: 0.9767 - val_loss: 3.0716 - val_categorical_accuracy: 0.5119 - lr: 0.0010\n",
      "Epoch 347/400\n",
      "47/47 [==============================] - 19s 397ms/step - loss: 0.0823 - categorical_accuracy: 0.9777 - val_loss: 2.6593 - val_categorical_accuracy: 0.5466 - lr: 0.0010\n",
      "Epoch 348/400\n",
      "47/47 [==============================] - 19s 396ms/step - loss: 0.1154 - categorical_accuracy: 0.9656 - val_loss: 2.4807 - val_categorical_accuracy: 0.5597 - lr: 0.0010\n",
      "Epoch 349/400\n",
      "47/47 [==============================] - 19s 397ms/step - loss: 0.0872 - categorical_accuracy: 0.9777 - val_loss: 2.5332 - val_categorical_accuracy: 0.5575 - lr: 0.0010\n",
      "Epoch 350/400\n",
      "47/47 [==============================] - 19s 398ms/step - loss: 0.0953 - categorical_accuracy: 0.9730 - val_loss: 2.7751 - val_categorical_accuracy: 0.5488 - lr: 0.0010\n",
      "Epoch 351/400\n",
      "47/47 [==============================] - 19s 400ms/step - loss: 0.1162 - categorical_accuracy: 0.9674 - val_loss: 2.8454 - val_categorical_accuracy: 0.5054 - lr: 0.0010\n",
      "Epoch 352/400\n",
      "47/47 [==============================] - 19s 409ms/step - loss: 0.0726 - categorical_accuracy: 0.9805 - val_loss: 2.6950 - val_categorical_accuracy: 0.5488 - lr: 0.0010\n",
      "Epoch 353/400\n",
      "47/47 [==============================] - 19s 396ms/step - loss: 0.0801 - categorical_accuracy: 0.9767 - val_loss: 2.7486 - val_categorical_accuracy: 0.5575 - lr: 0.0010\n",
      "Epoch 354/400\n",
      "47/47 [==============================] - 19s 397ms/step - loss: 0.1216 - categorical_accuracy: 0.9693 - val_loss: 2.6229 - val_categorical_accuracy: 0.5553 - lr: 0.0010\n",
      "Epoch 355/400\n",
      "47/47 [==============================] - 19s 403ms/step - loss: 0.1206 - categorical_accuracy: 0.9721 - val_loss: 2.6127 - val_categorical_accuracy: 0.5466 - lr: 0.0010\n",
      "Epoch 356/400\n",
      "47/47 [==============================] - 19s 396ms/step - loss: 0.0972 - categorical_accuracy: 0.9786 - val_loss: 2.5758 - val_categorical_accuracy: 0.5531 - lr: 0.0010\n",
      "Epoch 357/400\n",
      "47/47 [==============================] - 18s 393ms/step - loss: 0.1276 - categorical_accuracy: 0.9693 - val_loss: 2.5324 - val_categorical_accuracy: 0.5249 - lr: 0.0010\n",
      "Epoch 358/400\n",
      "47/47 [==============================] - 19s 397ms/step - loss: 0.0921 - categorical_accuracy: 0.9730 - val_loss: 2.5523 - val_categorical_accuracy: 0.5445 - lr: 0.0010\n",
      "Epoch 359/400\n",
      "47/47 [==============================] - 19s 395ms/step - loss: 0.0505 - categorical_accuracy: 0.9842 - val_loss: 2.5279 - val_categorical_accuracy: 0.5575 - lr: 0.0010\n",
      "Epoch 360/400\n",
      "47/47 [==============================] - 19s 396ms/step - loss: 0.0863 - categorical_accuracy: 0.9721 - val_loss: 2.7127 - val_categorical_accuracy: 0.5358 - lr: 0.0010\n",
      "Epoch 361/400\n",
      "47/47 [==============================] - 20s 422ms/step - loss: 0.2079 - categorical_accuracy: 0.9507 - val_loss: 2.6140 - val_categorical_accuracy: 0.5466 - lr: 0.0010\n",
      "Epoch 362/400\n",
      "47/47 [==============================] - 20s 417ms/step - loss: 0.1427 - categorical_accuracy: 0.9609 - val_loss: 2.5734 - val_categorical_accuracy: 0.5510 - lr: 0.0010\n",
      "Epoch 363/400\n",
      "47/47 [==============================] - 19s 411ms/step - loss: 0.1750 - categorical_accuracy: 0.9526 - val_loss: 2.5389 - val_categorical_accuracy: 0.5054 - lr: 0.0010\n",
      "Epoch 364/400\n",
      "47/47 [==============================] - 19s 398ms/step - loss: 0.2084 - categorical_accuracy: 0.9479 - val_loss: 2.3960 - val_categorical_accuracy: 0.5163 - lr: 0.0010\n",
      "Epoch 365/400\n",
      "47/47 [==============================] - 19s 402ms/step - loss: 0.1311 - categorical_accuracy: 0.9628 - val_loss: 2.2268 - val_categorical_accuracy: 0.5705 - lr: 0.0010\n",
      "Epoch 366/400\n",
      "47/47 [==============================] - 20s 430ms/step - loss: 0.1115 - categorical_accuracy: 0.9684 - val_loss: 2.4091 - val_categorical_accuracy: 0.5466 - lr: 0.0010\n",
      "Epoch 367/400\n",
      "47/47 [==============================] - 19s 401ms/step - loss: 0.1024 - categorical_accuracy: 0.9674 - val_loss: 2.5567 - val_categorical_accuracy: 0.5445 - lr: 0.0010\n",
      "Epoch 368/400\n",
      "47/47 [==============================] - 19s 397ms/step - loss: 0.1040 - categorical_accuracy: 0.9712 - val_loss: 2.5532 - val_categorical_accuracy: 0.5423 - lr: 0.0010\n",
      "Epoch 369/400\n",
      "47/47 [==============================] - 19s 397ms/step - loss: 0.1313 - categorical_accuracy: 0.9693 - val_loss: 2.3531 - val_categorical_accuracy: 0.5488 - lr: 0.0010\n",
      "Epoch 370/400\n",
      "47/47 [==============================] - 19s 399ms/step - loss: 0.1205 - categorical_accuracy: 0.9721 - val_loss: 2.3592 - val_categorical_accuracy: 0.5466 - lr: 0.0010\n",
      "Epoch 371/400\n",
      "47/47 [==============================] - 19s 410ms/step - loss: 0.0776 - categorical_accuracy: 0.9777 - val_loss: 2.3661 - val_categorical_accuracy: 0.5510 - lr: 0.0010\n",
      "Epoch 372/400\n",
      "47/47 [==============================] - 19s 397ms/step - loss: 0.0898 - categorical_accuracy: 0.9786 - val_loss: 2.5764 - val_categorical_accuracy: 0.5466 - lr: 0.0010\n",
      "Epoch 373/400\n",
      "47/47 [==============================] - 19s 399ms/step - loss: 0.0710 - categorical_accuracy: 0.9823 - val_loss: 2.6409 - val_categorical_accuracy: 0.5401 - lr: 0.0010\n",
      "Epoch 374/400\n",
      "47/47 [==============================] - 19s 398ms/step - loss: 0.0792 - categorical_accuracy: 0.9767 - val_loss: 2.6603 - val_categorical_accuracy: 0.5445 - lr: 0.0010\n",
      "Epoch 375/400\n",
      "47/47 [==============================] - 19s 397ms/step - loss: 0.0992 - categorical_accuracy: 0.9721 - val_loss: 2.4087 - val_categorical_accuracy: 0.5510 - lr: 0.0010\n",
      "Epoch 376/400\n",
      "47/47 [==============================] - 19s 398ms/step - loss: 0.0731 - categorical_accuracy: 0.9786 - val_loss: 2.5748 - val_categorical_accuracy: 0.5466 - lr: 0.0010\n",
      "Epoch 377/400\n",
      "47/47 [==============================] - 19s 399ms/step - loss: 0.0984 - categorical_accuracy: 0.9730 - val_loss: 2.5551 - val_categorical_accuracy: 0.5510 - lr: 0.0010\n",
      "Epoch 378/400\n",
      "47/47 [==============================] - 19s 397ms/step - loss: 0.0713 - categorical_accuracy: 0.9767 - val_loss: 2.5697 - val_categorical_accuracy: 0.5510 - lr: 0.0010\n",
      "Epoch 379/400\n",
      "47/47 [==============================] - 19s 397ms/step - loss: 0.1007 - categorical_accuracy: 0.9767 - val_loss: 2.6254 - val_categorical_accuracy: 0.5380 - lr: 0.0010\n",
      "Epoch 380/400\n",
      "47/47 [==============================] - 19s 398ms/step - loss: 0.0647 - categorical_accuracy: 0.9786 - val_loss: 2.7222 - val_categorical_accuracy: 0.5206 - lr: 0.0010\n",
      "Epoch 381/400\n",
      "47/47 [==============================] - 19s 398ms/step - loss: 0.0604 - categorical_accuracy: 0.9833 - val_loss: 2.6131 - val_categorical_accuracy: 0.5618 - lr: 0.0010\n",
      "Epoch 382/400\n",
      "47/47 [==============================] - 19s 401ms/step - loss: 0.0622 - categorical_accuracy: 0.9870 - val_loss: 2.7206 - val_categorical_accuracy: 0.5293 - lr: 0.0010\n",
      "Epoch 383/400\n",
      "47/47 [==============================] - 19s 399ms/step - loss: 0.0759 - categorical_accuracy: 0.9833 - val_loss: 2.7155 - val_categorical_accuracy: 0.5466 - lr: 0.0010\n",
      "Epoch 384/400\n",
      "47/47 [==============================] - 19s 401ms/step - loss: 0.0726 - categorical_accuracy: 0.9795 - val_loss: 2.6551 - val_categorical_accuracy: 0.5336 - lr: 0.0010\n",
      "Epoch 385/400\n",
      "47/47 [==============================] - 19s 402ms/step - loss: 0.0746 - categorical_accuracy: 0.9795 - val_loss: 2.7078 - val_categorical_accuracy: 0.5380 - lr: 0.0010\n",
      "Epoch 386/400\n",
      "47/47 [==============================] - 19s 399ms/step - loss: 0.0816 - categorical_accuracy: 0.9767 - val_loss: 2.6246 - val_categorical_accuracy: 0.5597 - lr: 0.0010\n",
      "Epoch 387/400\n",
      "47/47 [==============================] - 19s 399ms/step - loss: 0.0597 - categorical_accuracy: 0.9814 - val_loss: 2.8005 - val_categorical_accuracy: 0.5249 - lr: 0.0010\n",
      "Epoch 388/400\n",
      "47/47 [==============================] - 19s 401ms/step - loss: 0.0715 - categorical_accuracy: 0.9805 - val_loss: 2.6295 - val_categorical_accuracy: 0.5336 - lr: 0.0010\n",
      "Epoch 389/400\n",
      "47/47 [==============================] - 19s 399ms/step - loss: 0.0697 - categorical_accuracy: 0.9833 - val_loss: 2.7259 - val_categorical_accuracy: 0.5618 - lr: 0.0010\n",
      "Epoch 390/400\n",
      "47/47 [==============================] - 19s 401ms/step - loss: 0.0753 - categorical_accuracy: 0.9805 - val_loss: 2.7546 - val_categorical_accuracy: 0.5597 - lr: 0.0010\n",
      "Epoch 391/400\n",
      "47/47 [==============================] - 19s 401ms/step - loss: 0.0667 - categorical_accuracy: 0.9842 - val_loss: 2.6748 - val_categorical_accuracy: 0.5488 - lr: 0.0010\n",
      "Epoch 392/400\n",
      "47/47 [==============================] - 19s 401ms/step - loss: 0.0586 - categorical_accuracy: 0.9833 - val_loss: 2.9178 - val_categorical_accuracy: 0.5249 - lr: 0.0010\n",
      "Epoch 393/400\n",
      "47/47 [==============================] - 19s 402ms/step - loss: 0.0887 - categorical_accuracy: 0.9767 - val_loss: 2.6702 - val_categorical_accuracy: 0.5510 - lr: 0.0010\n",
      "Epoch 394/400\n",
      "47/47 [==============================] - 19s 405ms/step - loss: 0.0518 - categorical_accuracy: 0.9879 - val_loss: 2.7799 - val_categorical_accuracy: 0.5531 - lr: 0.0010\n",
      "Epoch 395/400\n",
      "47/47 [==============================] - 19s 401ms/step - loss: 0.0388 - categorical_accuracy: 0.9916 - val_loss: 2.7742 - val_categorical_accuracy: 0.5640 - lr: 0.0010\n",
      "Epoch 396/400\n",
      "47/47 [==============================] - 19s 401ms/step - loss: 0.0787 - categorical_accuracy: 0.9805 - val_loss: 2.8576 - val_categorical_accuracy: 0.5401 - lr: 0.0010\n",
      "Epoch 397/400\n",
      "47/47 [==============================] - 19s 402ms/step - loss: 0.0687 - categorical_accuracy: 0.9851 - val_loss: 2.7787 - val_categorical_accuracy: 0.5553 - lr: 0.0010\n",
      "Epoch 398/400\n",
      "47/47 [==============================] - 19s 402ms/step - loss: 0.0833 - categorical_accuracy: 0.9786 - val_loss: 2.7459 - val_categorical_accuracy: 0.5575 - lr: 0.0010\n",
      "Epoch 399/400\n",
      "47/47 [==============================] - 19s 400ms/step - loss: 0.0700 - categorical_accuracy: 0.9823 - val_loss: 2.9928 - val_categorical_accuracy: 0.5293 - lr: 0.0010\n",
      "Epoch 400/400\n",
      "47/47 [==============================] - 19s 400ms/step - loss: 0.1118 - categorical_accuracy: 0.9730 - val_loss: 2.7589 - val_categorical_accuracy: 0.5423 - lr: 0.0010\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "model.add(layers.LSTM(64, return_sequences = True, input_shape=(new_mfcc_features.shape[1:3])))\n",
    "model.add(layers.LSTM(64))\n",
    "model.add(layers.Dense(8, activation = 'softmax'))\n",
    "print(model.summary())\n",
    "\n",
    "batch_size = 23\n",
    "\n",
    "# Callbacks functions\n",
    "checkpoint_path = '/content/drive/My Drive/Colab Notebooks/best_weights.hdf5'\n",
    "\n",
    "#-> Save the best weights\n",
    "mcp_save = callbacks.ModelCheckpoint(checkpoint_path, save_best_only=True,\n",
    "                           monitor='val_categorical_accuracy',\n",
    "                           mode='max')\n",
    "#-> Reduce learning rate after 100 epoches without improvement.\n",
    "rlrop = callbacks.ReduceLROnPlateau(monitor='val_categorical_accuracy', \n",
    "                                    factor=0.1, patience=100)\n",
    "                             \n",
    "# Compile & train   \n",
    "model.compile(loss='categorical_crossentropy', \n",
    "                optimizer='RMSProp', \n",
    "                metrics=['categorical_accuracy'])\n",
    "\n",
    "history = model.fit(X_train, y_train_onehot_encoded, \n",
    "                      epochs=400, batch_size = batch_size, \n",
    "                      validation_data = (X_test, y_test_onehot_encoded), \n",
    "                      callbacks = [mcp_save, rlrop])\n",
    "# Define the best weights to the model.\n",
    "model.load_weights(checkpoint_path)"
   ]
  }
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