Lab10,11/HMM-LSTM.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hidden Markov Model Program on Long Short-Term Memory (LSTM) Networks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-04-04 04:01:51.298965: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2024-04-04 04:01:55.071471: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n"
     ]
    }
   ],
   "source": [
    "# Importing the necessary libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn import datasets\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.metrics import confusion_matrix\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Libraries for building the LSTM model\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.layers import LSTM\n",
    "from keras.layers import Dropout"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load and Preprocess the Iris Dataset\n",
    "Load the Iris dataset from sklearn, perform necessary preprocessing such as normalization, train-test split, etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the Iris dataset\n",
    "iris = datasets.load_iris()\n",
    "X = iris.data\n",
    "y = iris.target\n",
    "\n",
    "# Normalize the features\n",
    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "X = scaler.fit_transform(X)\n",
    "\n",
    "# Perform train-test split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Reshape input to be [samples, time steps, features] which is required for LSTM\n",
    "X_train = np.reshape(X_train, (X_train.shape[0], 1, X_train.shape[1]))\n",
    "X_test = np.reshape(X_test, (X_test.shape[0], 1, X_test.shape[1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Define the LSTM Model\n",
    "Define the LSTM model architecture using keras. The model should be suitable for the sequential modelling task."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/codespace/.local/lib/python3.10/site-packages/keras/src/layers/rnn/rnn.py:204: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(**kwargs)\n"
     ]
    }
   ],
   "source": [
    "# Define the LSTM Model\n",
    "model = Sequential()\n",
    "\n",
    "# Adding the first LSTM layer and some Dropout regularisation\n",
    "model.add(LSTM(units = 50, return_sequences = True, input_shape = (X_train.shape[1], X_train.shape[2])))\n",
    "model.add(Dropout(0.2))\n",
    "\n",
    "# Adding a second LSTM layer and some Dropout regularisation\n",
    "model.add(LSTM(units = 50, return_sequences = True))\n",
    "model.add(Dropout(0.2))\n",
    "\n",
    "# Adding a third LSTM layer and some Dropout regularisation\n",
    "model.add(LSTM(units = 50, return_sequences = True))\n",
    "model.add(Dropout(0.2))\n",
    "\n",
    "# Adding a fourth LSTM layer and some Dropout regularisation\n",
    "model.add(LSTM(units = 50))\n",
    "model.add(Dropout(0.2))\n",
    "\n",
    "# Adding the output layer\n",
    "model.add(Dense(units = 3))\n",
    "\n",
    "# Compiling the LSTM\n",
    "model.compile(optimizer = 'adam', loss = 'sparse_categorical_crossentropy', metrics = ['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train the LSTM Model\n",
    "Train the LSTM model on the preprocessed Iris dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 5ms/step - accuracy: 0.3108 - loss: 4.1149\n",
      "Epoch 2/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.3394 - loss: 4.1676 \n",
      "Epoch 3/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.3421 - loss: 4.4025 \n",
      "Epoch 4/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.3019 - loss: 4.5187 \n",
      "Epoch 5/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.3510 - loss: 4.3722 \n",
      "Epoch 6/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.3613 - loss: 4.4838 \n",
      "Epoch 7/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.3135 - loss: 4.5755 \n",
      "Epoch 8/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.3781 - loss: 4.6192 \n",
      "Epoch 9/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.3529 - loss: 4.7875 \n",
      "Epoch 10/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.2633 - loss: 4.1997 \n",
      "Epoch 11/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.3862 - loss: 4.6583 \n",
      "Epoch 12/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.3804 - loss: 4.6059 \n",
      "Epoch 13/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.3408 - loss: 4.8162 \n",
      "Epoch 14/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.3604 - loss: 4.7680 \n",
      "Epoch 15/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.3946 - loss: 4.7783 \n",
      "Epoch 16/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.4348 - loss: 3.9995 \n",
      "Epoch 17/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.4263 - loss: 4.4601  \n",
      "Epoch 18/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.4302 - loss: 4.3247 \n",
      "Epoch 19/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.4044 - loss: 4.4910 \n",
      "Epoch 20/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.4171 - loss: 4.2786 \n",
      "Epoch 21/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.3931 - loss: 4.2764 \n",
      "Epoch 22/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.4152 - loss: 4.3435 \n",
      "Epoch 23/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.4471 - loss: 4.0896 \n",
      "Epoch 24/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.4102 - loss: 3.8876 \n",
      "Epoch 25/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.4004 - loss: 3.4812 \n",
      "Epoch 26/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.4392 - loss: 2.6333 \n",
      "Epoch 27/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.4250 - loss: 1.4836 \n",
      "Epoch 28/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.4398 - loss: 1.2587 \n",
      "Epoch 29/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.4017 - loss: 1.2330 \n",
      "Epoch 30/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.4969 - loss: 1.2940 \n",
      "Epoch 31/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.4300 - loss: 1.0869 \n",
      "Epoch 32/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.5002 - loss: 1.2984 \n",
      "Epoch 33/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.4700 - loss: 1.0735 \n",
      "Epoch 34/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.4642 - loss: 1.0895 \n",
      "Epoch 35/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.4325 - loss: 1.3184 \n",
      "Epoch 36/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.3827 - loss: 1.3147 \n",
      "Epoch 37/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.2865 - loss: 2.0339 \n",
      "Epoch 38/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.3490 - loss: 2.7268 \n",
      "Epoch 39/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.2969 - loss: 3.4768 \n",
      "Epoch 40/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.3304 - loss: 3.1743 \n",
      "Epoch 41/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.3623 - loss: 1.6454 \n",
      "Epoch 42/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.3694 - loss: 1.2876 \n",
      "Epoch 43/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.4804 - loss: 1.1816 \n",
      "Epoch 44/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.4692 - loss: 1.0876 \n",
      "Epoch 45/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.5898 - loss: 1.0519 \n",
      "Epoch 46/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.5929 - loss: 1.0500 \n",
      "Epoch 47/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.6008 - loss: 1.0849 \n",
      "Epoch 48/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.5979 - loss: 1.0977 \n",
      "Epoch 49/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.5421 - loss: 1.0769 \n",
      "Epoch 50/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.5881 - loss: 1.1928 \n",
      "Epoch 51/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7135 - loss: 1.1101 \n",
      "Epoch 52/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.6517 - loss: 1.0940 \n",
      "Epoch 53/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.5785 - loss: 1.1366 \n",
      "Epoch 54/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.5273 - loss: 1.2184 \n",
      "Epoch 55/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.6035 - loss: 1.0734 \n",
      "Epoch 56/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.6067 - loss: 1.1320 \n",
      "Epoch 57/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.5733 - loss: 1.0677 \n",
      "Epoch 58/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.5575 - loss: 1.0895 \n",
      "Epoch 59/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.5954 - loss: 1.0826 \n",
      "Epoch 60/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.6329 - loss: 1.0986 \n",
      "Epoch 61/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.6188 - loss: 1.0689 \n",
      "Epoch 62/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6012 - loss: 1.0940 \n",
      "Epoch 63/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.6000 - loss: 1.0986 \n",
      "Epoch 64/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.5569 - loss: 1.0780 \n",
      "Epoch 65/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.5400 - loss: 1.0986 \n",
      "Epoch 66/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6971 - loss: 1.0897 \n",
      "Epoch 67/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.5735 - loss: 1.0986 \n",
      "Epoch 68/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.6281 - loss: 1.0940 \n",
      "Epoch 69/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.5692 - loss: 1.2175 \n",
      "Epoch 70/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.6698 - loss: 1.0889 \n",
      "Epoch 71/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.6185 - loss: 1.0820 \n",
      "Epoch 72/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.6012 - loss: 1.0826 \n",
      "Epoch 73/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.5337 - loss: 1.0817 \n",
      "Epoch 74/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6862 - loss: 1.0837 \n",
      "Epoch 75/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.5915 - loss: 1.1132 \n",
      "Epoch 76/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6438 - loss: 1.0483 \n",
      "Epoch 77/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.6610 - loss: 1.0929 \n",
      "Epoch 78/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.5910 - loss: 1.0826 \n",
      "Epoch 79/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.5602 - loss: 1.0769 \n",
      "Epoch 80/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6158 - loss: 1.0826 \n",
      "Epoch 81/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6075 - loss: 1.0734 \n",
      "Epoch 82/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.5913 - loss: 1.0929 \n",
      "Epoch 83/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.5521 - loss: 1.0849 \n",
      "Epoch 84/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.5729 - loss: 1.0986 \n",
      "Epoch 85/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - accuracy: 0.5425 - loss: 1.0986\n",
      "Epoch 86/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.6302 - loss: 1.0826 \n",
      "Epoch 87/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.6575 - loss: 1.0804 \n",
      "Epoch 88/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.5915 - loss: 1.0756 \n",
      "Epoch 89/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.5602 - loss: 1.0986 \n",
      "Epoch 90/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.6285 - loss: 1.0826 \n",
      "Epoch 91/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.5190 - loss: 1.0883 \n",
      "Epoch 92/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6573 - loss: 1.0940 \n",
      "Epoch 93/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.6254 - loss: 1.0929 \n",
      "Epoch 94/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.5000 - loss: 1.2212 \n",
      "Epoch 95/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.6698 - loss: 1.0735 \n",
      "Epoch 96/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6500 - loss: 1.0929 \n",
      "Epoch 97/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6006 - loss: 1.0986 \n",
      "Epoch 98/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.5971 - loss: 1.0666 \n",
      "Epoch 99/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.5667 - loss: 1.0929 \n",
      "Epoch 100/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.6660 - loss: 1.0986 \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.src.callbacks.history.History at 0x7f7b95f280a0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train the LSTM Model\n",
    "model.fit(X_train, y_train, epochs = 100, batch_size = 32)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluate the LSTM Model\n",
    "Evaluate the performance of the trained LSTM model on the test data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy:  0.3333333333333333\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Evaluate the LSTM Model\n",
    "# Predict the values for test data\n",
    "y_pred = model.predict(X_test)\n",
    "y_pred = np.argmax(y_pred, axis=1)\n",
    "\n",
    "# Generate the confusion matrix\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "# Plot the confusion matrix\n",
    "plt.figure(figsize=(10,7))\n",
    "sns.heatmap(cm, annot=True)\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('Truth')\n",
    "\n",
    "# Calculate the accuracy of the model\n",
    "accuracy = (cm[0][0] + cm[1][1]) / cm.sum()\n",
    "print(\"Accuracy: \", accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot Model Accuracy and Loss\n",
    "Plot the model accuracy and loss over the training epochs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 175ms/step - accuracy: 0.6290 - loss: 1.0705 - val_accuracy: 0.7000 - val_loss: 1.0986\n",
      "Epoch 2/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.6446 - loss: 1.0986 - val_accuracy: 0.7000 - val_loss: 1.0986\n",
      "Epoch 3/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.6671 - loss: 1.0803 - val_accuracy: 0.7000 - val_loss: 1.0986\n",
      "Epoch 4/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.5681 - loss: 1.0837 - val_accuracy: 0.7000 - val_loss: 1.0986\n",
      "Epoch 5/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.5998 - loss: 1.1377 - val_accuracy: 0.7000 - val_loss: 1.0986\n",
      "Epoch 6/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - accuracy: 0.5804 - loss: 1.0986 - val_accuracy: 0.7000 - val_loss: 1.0986\n",
      "Epoch 7/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.6146 - loss: 1.0797 - val_accuracy: 0.7000 - val_loss: 1.0986\n",
      "Epoch 8/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.6354 - loss: 1.0949 - val_accuracy: 0.7000 - val_loss: 1.0986\n",
      "Epoch 9/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.5667 - loss: 1.0689 - val_accuracy: 0.7000 - val_loss: 1.0986\n",
      "Epoch 10/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.6402 - loss: 1.0757 - val_accuracy: 0.7000 - val_loss: 1.0986\n",
      "Epoch 11/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - accuracy: 0.5885 - loss: 1.0666 - val_accuracy: 0.7000 - val_loss: 1.0986\n",
      "Epoch 12/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.6654 - loss: 1.0883 - val_accuracy: 0.7000 - val_loss: 1.0986\n",
      "Epoch 13/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.6277 - loss: 1.0837 - val_accuracy: 0.7000 - val_loss: 1.0986\n",
      "Epoch 14/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.5735 - loss: 1.0734 - val_accuracy: 0.7000 - val_loss: 1.0986\n",
      "Epoch 15/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - accuracy: 0.5717 - loss: 1.1372 - val_accuracy: 0.7000 - val_loss: 1.0986\n",
      "Epoch 16/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - accuracy: 0.6754 - loss: 1.0797 - val_accuracy: 0.7000 - val_loss: 1.0986\n",
      "Epoch 17/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.6319 - loss: 1.0929 - val_accuracy: 0.7000 - val_loss: 1.0986\n",
      "Epoch 18/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.6446 - loss: 1.0803 - val_accuracy: 0.7000 - val_loss: 1.0986\n",
      "Epoch 19/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.5315 - loss: 1.0780 - val_accuracy: 0.7000 - val_loss: 1.0986\n",
      "Epoch 20/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.6410 - loss: 1.0986 - val_accuracy: 0.7000 - val_loss: 1.0986\n",
      "Epoch 21/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.5696 - loss: 1.0945 - val_accuracy: 0.7000 - val_loss: 1.0986\n",
      "Epoch 22/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.6279 - loss: 1.0734 - val_accuracy: 0.7000 - val_loss: 1.0986\n",
      "Epoch 23/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.6431 - loss: 1.0849 - val_accuracy: 0.7000 - val_loss: 1.0986\n",
      "Epoch 24/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.5517 - loss: 1.0941 - val_accuracy: 0.7000 - val_loss: 1.0986\n",
      "Epoch 25/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - accuracy: 0.6608 - loss: 1.0986 - val_accuracy: 0.7000 - val_loss: 1.0986\n",
      "Epoch 26/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - accuracy: 0.6269 - loss: 1.0872 - val_accuracy: 0.7000 - val_loss: 1.0986\n",
      "Epoch 27/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.5396 - loss: 1.0986 - val_accuracy: 0.7000 - val_loss: 1.0986\n",
      "Epoch 28/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.6338 - loss: 1.0854 - val_accuracy: 0.7000 - val_loss: 1.0986\n",
      "Epoch 29/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.5917 - loss: 1.0895 - val_accuracy: 0.7000 - val_loss: 1.0986\n",
      "Epoch 30/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.5196 - loss: 1.0929 - val_accuracy: 0.7000 - val_loss: 1.0986\n",
      "Epoch 31/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.6283 - loss: 1.0837 - val_accuracy: 0.7000 - val_loss: 1.0986\n",
      "Epoch 32/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.6244 - loss: 1.0986 - val_accuracy: 0.7000 - val_loss: 1.0986\n",
      "Epoch 33/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.6275 - loss: 1.0826 - val_accuracy: 0.7000 - val_loss: 1.0986\n",
      "Epoch 34/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - accuracy: 0.5817 - loss: 1.0796 - val_accuracy: 0.7000 - val_loss: 1.0986\n",
      "Epoch 35/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.6223 - loss: 1.0883 - val_accuracy: 0.7000 - val_loss: 1.0986\n",
      "Epoch 36/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.6017 - loss: 1.0929 - val_accuracy: 0.7000 - val_loss: 1.0986\n",
      "Epoch 37/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.5663 - loss: 1.0853 - val_accuracy: 0.7000 - val_loss: 1.0986\n",
      "Epoch 38/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.5767 - loss: 1.0884 - val_accuracy: 0.7000 - val_loss: 1.0986\n",
      "Epoch 39/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.6640 - loss: 1.0986 - val_accuracy: 0.7000 - val_loss: 1.0986\n",
      "Epoch 40/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.5721 - loss: 1.0826 - val_accuracy: 0.7000 - val_loss: 1.0986\n",
      "Epoch 41/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.5600 - loss: 1.0957 - val_accuracy: 0.7000 - val_loss: 1.0986\n",
      "Epoch 42/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.6998 - loss: 1.0609 - val_accuracy: 0.7000 - val_loss: 1.0986\n",
      "Epoch 43/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.5777 - loss: 1.0895 - val_accuracy: 0.7000 - val_loss: 1.0986\n",
      "Epoch 44/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - accuracy: 0.6238 - loss: 1.0940 - val_accuracy: 0.7000 - val_loss: 1.0986\n",
      "Epoch 45/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.6171 - loss: 1.0734 - val_accuracy: 0.7000 - val_loss: 1.0986\n",
      "Epoch 46/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - accuracy: 0.6623 - loss: 1.0895 - val_accuracy: 0.7000 - val_loss: 1.0986\n",
      "Epoch 47/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.5910 - loss: 1.0986 - val_accuracy: 0.7000 - val_loss: 1.0986\n",
      "Epoch 48/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.5319 - loss: 1.0986 - val_accuracy: 0.7000 - val_loss: 1.0986\n",
      "Epoch 49/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.5394 - loss: 1.0958 - val_accuracy: 0.7000 - val_loss: 1.0986\n",
      "Epoch 50/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.6110 - loss: 1.0986 - val_accuracy: 0.7000 - val_loss: 1.0986\n",
      "Epoch 51/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.6340 - loss: 1.0597 - val_accuracy: 0.7000 - val_loss: 1.0986\n",
      "Epoch 52/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.6533 - loss: 1.0895 - val_accuracy: 0.7000 - val_loss: 1.0986\n",
      "Epoch 53/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.5840 - loss: 1.0929 - val_accuracy: 0.7000 - val_loss: 1.0986\n",
      "Epoch 54/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.6604 - loss: 1.0827 - val_accuracy: 0.7000 - val_loss: 1.0986\n",
      "Epoch 55/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.6292 - loss: 1.0677 - val_accuracy: 0.7000 - val_loss: 1.0986\n",
      "Epoch 56/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.6594 - loss: 1.0826 - val_accuracy: 0.7000 - val_loss: 1.0986\n",
      "Epoch 57/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.5879 - loss: 1.0522 - val_accuracy: 0.7000 - val_loss: 1.0986\n",
      "Epoch 58/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.4892 - loss: 1.0826 - val_accuracy: 0.7000 - val_loss: 1.0986\n",
      "Epoch 59/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.5483 - loss: 1.0826 - val_accuracy: 0.7000 - val_loss: 1.0986\n",
      "Epoch 60/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.6140 - loss: 1.0986 - val_accuracy: 0.7000 - val_loss: 1.0986\n",
      "Epoch 61/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.6485 - loss: 1.0929 - val_accuracy: 0.7000 - val_loss: 1.0986\n",
      "Epoch 62/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.5865 - loss: 1.0903 - val_accuracy: 0.7000 - val_loss: 1.0986\n",
      "Epoch 63/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.5302 - loss: 1.0986 - val_accuracy: 0.7000 - val_loss: 1.0986\n",
      "Epoch 64/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - accuracy: 0.5542 - loss: 1.0643 - val_accuracy: 0.7000 - val_loss: 1.0986\n",
      "Epoch 65/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.5746 - loss: 1.0929 - val_accuracy: 0.7000 - val_loss: 1.0986\n",
      "Epoch 66/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.5542 - loss: 1.0940 - val_accuracy: 0.7000 - val_loss: 1.0986\n",
      "Epoch 67/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.6198 - loss: 1.0609 - val_accuracy: 0.7000 - val_loss: 1.0986\n",
      "Epoch 68/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.5835 - loss: 1.0780 - val_accuracy: 0.7000 - val_loss: 1.0986\n",
      "Epoch 69/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.6394 - loss: 1.0677 - val_accuracy: 0.7000 - val_loss: 1.0986\n",
      "Epoch 70/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.6094 - loss: 1.0849 - val_accuracy: 0.7000 - val_loss: 1.0986\n",
      "Epoch 71/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - accuracy: 0.6435 - loss: 1.0849 - val_accuracy: 0.7000 - val_loss: 1.0986\n",
      "Epoch 72/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - accuracy: 0.5871 - loss: 1.0986 - val_accuracy: 0.7000 - val_loss: 1.0986\n",
      "Epoch 73/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - accuracy: 0.5485 - loss: 1.0666 - val_accuracy: 0.7000 - val_loss: 1.0986\n",
      "Epoch 74/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step - accuracy: 0.6246 - loss: 1.0837 - val_accuracy: 0.7000 - val_loss: 1.0986\n",
      "Epoch 75/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - accuracy: 0.6521 - loss: 1.0883 - val_accuracy: 0.7000 - val_loss: 1.0986\n",
      "Epoch 76/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.6383 - loss: 1.0849 - val_accuracy: 0.7000 - val_loss: 1.0986\n",
      "Epoch 77/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.6702 - loss: 1.0880 - val_accuracy: 0.7000 - val_loss: 1.0986\n",
      "Epoch 78/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.6121 - loss: 1.0895 - val_accuracy: 0.7000 - val_loss: 1.0986\n",
      "Epoch 79/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.6292 - loss: 1.0929 - val_accuracy: 0.7000 - val_loss: 1.0986\n",
      "Epoch 80/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.6827 - loss: 1.0563 - val_accuracy: 0.7000 - val_loss: 1.0986\n",
      "Epoch 81/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.6052 - loss: 1.0929 - val_accuracy: 0.7000 - val_loss: 1.0986\n",
      "Epoch 82/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.6665 - loss: 1.2119 - val_accuracy: 0.7000 - val_loss: 1.0986\n",
      "Epoch 83/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.6421 - loss: 1.0940 - val_accuracy: 0.7000 - val_loss: 1.0986\n",
      "Epoch 84/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - accuracy: 0.5577 - loss: 1.0929 - val_accuracy: 0.7000 - val_loss: 1.0986\n",
      "Epoch 85/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - accuracy: 0.5850 - loss: 1.0986 - val_accuracy: 0.7000 - val_loss: 1.0986\n",
      "Epoch 86/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - accuracy: 0.6442 - loss: 1.0929 - val_accuracy: 0.7000 - val_loss: 1.0986\n",
      "Epoch 87/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - accuracy: 0.6771 - loss: 1.0437 - val_accuracy: 0.7000 - val_loss: 1.0986\n",
      "Epoch 88/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.5938 - loss: 1.0734 - val_accuracy: 0.7000 - val_loss: 1.0986\n",
      "Epoch 89/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.5844 - loss: 1.0837 - val_accuracy: 0.7000 - val_loss: 1.0986\n",
      "Epoch 90/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step - accuracy: 0.6587 - loss: 1.0929 - val_accuracy: 0.7000 - val_loss: 1.0986\n",
      "Epoch 91/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.6317 - loss: 1.0986 - val_accuracy: 0.7000 - val_loss: 1.0986\n",
      "Epoch 92/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.5575 - loss: 1.0816 - val_accuracy: 0.7000 - val_loss: 1.0986\n",
      "Epoch 93/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.6821 - loss: 1.0734 - val_accuracy: 0.7000 - val_loss: 1.0986\n",
      "Epoch 94/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.6242 - loss: 1.0610 - val_accuracy: 0.7000 - val_loss: 1.0986\n",
      "Epoch 95/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - accuracy: 0.5669 - loss: 1.0726 - val_accuracy: 0.7000 - val_loss: 1.0986\n",
      "Epoch 96/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.6229 - loss: 1.0884 - val_accuracy: 0.7000 - val_loss: 1.0986\n",
      "Epoch 97/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.5719 - loss: 0.9865 - val_accuracy: 0.7000 - val_loss: 1.0986\n",
      "Epoch 98/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.5754 - loss: 1.0185 - val_accuracy: 0.7000 - val_loss: 1.0986\n",
      "Epoch 99/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.5344 - loss: 0.9693 - val_accuracy: 0.6667 - val_loss: 0.9158\n",
      "Epoch 100/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.5090 - loss: 1.0902 - val_accuracy: 0.6667 - val_loss: 0.8060\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the model accuracy and loss\n",
    "history = model.fit(X_train, y_train, epochs = 100, batch_size = 32, validation_data=(X_test, y_test))\n",
    "\n",
    "# Summarize history for accuracy\n",
    "plt.figure(figsize=(10,5))\n",
    "plt.plot(history.history['accuracy'])\n",
    "plt.plot(history.history['val_accuracy'])\n",
    "plt.title('Model Accuracy')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['Train', 'Test'], loc='upper left')\n",
    "plt.show()\n",
    "\n",
    "# Summarize history for loss\n",
    "plt.figure(figsize=(10,5))\n",
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('Model Loss')\n",
    "plt.ylabel('Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['Train', 'Test'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Print Classification Report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification Report:\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00        10\n",
      "           1       0.00      0.00      0.00         9\n",
      "           2       0.55      1.00      0.71        11\n",
      "\n",
      "    accuracy                           0.70        30\n",
      "   macro avg       0.52      0.67      0.57        30\n",
      "weighted avg       0.54      0.70      0.59        30\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/codespace/.local/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1509: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
      "/home/codespace/.local/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1509: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
      "/home/codespace/.local/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1509: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "\n",
    "print('Classification Report:\\n')\n",
    "print(classification_report(y_test, y_pred))"
   ]
  }
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