Notebooks/TensorFlow/ANN/TF_ANN-I.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1 style=\"color:rgb(0,120,170)\">Neural Networks and Deep Learning</h1>\n",
    "<h2 style=\"color:rgb(0,120,170)\">Intro to Tensor Flow and Keras - simple model</h2>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: tensorflow in /home/renato/Documents/default-env/lib/python3.12/site-packages (2.17.0)\n",
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      "Collecting tensorboard\n",
      "  Using cached tensorboard-2.18.0-py3-none-any.whl.metadata (1.6 kB)\n",
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     ]
    }
   ],
   "source": [
    "!pip install -U tensorflow tensorboard"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-10-21 23:36:26.721384: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:485] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
      "2024-10-21 23:36:26.747818: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:8454] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
      "2024-10-21 23:36:26.754155: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1452] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
      "2024-10-21 23:36:26.774797: 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",
      "2024-10-21 23:36:27.642255: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import sys\n",
    "import re\n",
    "import time\n",
    "import datetime\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set_style(\"darkgrid\")\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorboard.plugins.hparams import api_pb2\n",
    "from tensorboard.plugins.hparams import summary as hparams_summary\n",
    "\n",
    "from google.protobuf import struct_pb2\n",
    "\n",
    "%load_ext tensorboard\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.17.0\n",
      "[]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-10-21 23:36:28.319858: E external/local_xla/xla/stream_executor/cuda/cuda_driver.cc:266] failed call to cuInit: CUDA_ERROR_NO_DEVICE: no CUDA-capable device is detected\n",
      "2024-10-21 23:36:28.319880: I external/local_xla/xla/stream_executor/cuda/cuda_diagnostics.cc:135] retrieving CUDA diagnostic information for host: vienna\n",
      "2024-10-21 23:36:28.319887: I external/local_xla/xla/stream_executor/cuda/cuda_diagnostics.cc:142] hostname: vienna\n",
      "2024-10-21 23:36:28.319969: I external/local_xla/xla/stream_executor/cuda/cuda_diagnostics.cc:166] libcuda reported version is: 550.120.0\n",
      "2024-10-21 23:36:28.319987: I external/local_xla/xla/stream_executor/cuda/cuda_diagnostics.cc:170] kernel reported version is: 550.120.0\n",
      "2024-10-21 23:36:28.319993: I external/local_xla/xla/stream_executor/cuda/cuda_diagnostics.cc:249] kernel version seems to match DSO: 550.120.0\n"
     ]
    }
   ],
   "source": [
    "print(tf.__version__)\n",
    "\n",
    "os.environ['CUDA_VISIBLE_DEVICES'] = \"0\"\n",
    "\n",
    "try:\n",
    "    physical_devices = tf.config.list_physical_devices('GPU') \n",
    "    #tf.config.experimental.set_memory_growth(physical_devices[0], True)\n",
    "    for device in physical_devices:\n",
    "        tf.config.experimental.set_memory_growth(device, True)\n",
    "    print(physical_devices)\n",
    "except:\n",
    "    print(\"No GPU\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fix random seed for reproducibility\n",
    "seed = 7\n",
    "np.random.seed(seed)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we'll develop a small ANN with Keras:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load pima indians dataset\n",
    "dataset = np.loadtxt(\"../../../data/pima-indians-diabetes.csv\", delimiter=\",\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  6.   , 148.   ,  72.   , ...,   0.627,  50.   ,   1.   ],\n",
       "       [  1.   ,  85.   ,  66.   , ...,   0.351,  31.   ,   0.   ],\n",
       "       [  8.   , 183.   ,  64.   , ...,   0.672,  32.   ,   1.   ],\n",
       "       ...,\n",
       "       [  5.   , 121.   ,  72.   , ...,   0.245,  30.   ,   0.   ],\n",
       "       [  1.   , 126.   ,  60.   , ...,   0.349,  47.   ,   1.   ],\n",
       "       [  1.   ,  93.   ,  70.   , ...,   0.315,  23.   ,   0.   ]])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# split into input (X) and output (Y) variables\n",
    "X = dataset[:,0:8]\n",
    "Y = dataset[:,8]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(768, 8)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(768,)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#https://www.tensorflow.org/api_docs/python/tf/keras/layers\n",
    "#https://www.tensorflow.org/api_docs/python/tf/keras/activations\n",
    "#https://www.tensorflow.org/api_docs/python/tf/keras/initializers\n",
    "#https://www.tensorflow.org/api_docs/python/tf/keras/losses\n",
    "#https://www.tensorflow.org/api_docs/python/tf/keras/optimizers\n",
    "#https://www.tensorflow.org/api_docs/python/tf/keras/metrics\n",
    "\n",
    "# Create model\n",
    "model = tf.keras.Sequential()\n",
    "\n",
    "model.add(tf.keras.layers.Dense(12, input_dim=8, activation=\"relu\", kernel_initializer=\"uniform\"))\n",
    "model.add(tf.keras.layers.Dense(8, activation=\"relu\", kernel_initializer=\"uniform\"))\n",
    "model.add(tf.keras.layers.Dense(1, activation=\"sigmoid\", kernel_initializer=\"uniform\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compile model\n",
    "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>)             │           <span style=\"color: #00af00; text-decoration-color: #00af00\">108</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>)              │           <span style=\"color: #00af00; text-decoration-color: #00af00\">104</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)              │             <span style=\"color: #00af00; text-decoration-color: #00af00\">9</span> │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ dense (\u001b[38;5;33mDense\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m)             │           \u001b[38;5;34m108\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_1 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m)              │           \u001b[38;5;34m104\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_2 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │             \u001b[38;5;34m9\u001b[0m │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">221</span> (884.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m221\u001b[0m (884.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">221</span> (884.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m221\u001b[0m (884.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 21ms/step - accuracy: 0.6664 - loss: 0.6917 - val_accuracy: 0.6429 - val_loss: 0.6889\n",
      "Epoch 2/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6383 - loss: 0.6874 - val_accuracy: 0.6429 - val_loss: 0.6833\n",
      "Epoch 3/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6496 - loss: 0.6802 - val_accuracy: 0.6429 - val_loss: 0.6760\n",
      "Epoch 4/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6405 - loss: 0.6761 - val_accuracy: 0.6429 - val_loss: 0.6711\n",
      "Epoch 5/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6667 - loss: 0.6629 - val_accuracy: 0.6429 - val_loss: 0.6699\n",
      "Epoch 6/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6561 - loss: 0.6618 - val_accuracy: 0.6429 - val_loss: 0.6699\n",
      "Epoch 7/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6905 - loss: 0.6417 - val_accuracy: 0.6429 - val_loss: 0.6699\n",
      "Epoch 8/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6499 - loss: 0.6617 - val_accuracy: 0.6429 - val_loss: 0.6701\n",
      "Epoch 9/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6425 - loss: 0.6597 - val_accuracy: 0.6429 - val_loss: 0.6704\n",
      "Epoch 10/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6404 - loss: 0.6577 - val_accuracy: 0.6429 - val_loss: 0.6711\n",
      "Epoch 11/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.6549 - loss: 0.6449 - val_accuracy: 0.6429 - val_loss: 0.6715\n",
      "Epoch 12/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6437 - loss: 0.6543 - val_accuracy: 0.6429 - val_loss: 0.6691\n",
      "Epoch 13/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6379 - loss: 0.6536 - val_accuracy: 0.6429 - val_loss: 0.6684\n",
      "Epoch 14/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6630 - loss: 0.6373 - val_accuracy: 0.6429 - val_loss: 0.6682\n",
      "Epoch 15/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6669 - loss: 0.6299 - val_accuracy: 0.6429 - val_loss: 0.6651\n",
      "Epoch 16/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.6575 - loss: 0.6387 - val_accuracy: 0.6429 - val_loss: 0.6621\n",
      "Epoch 17/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6656 - loss: 0.6313 - val_accuracy: 0.6429 - val_loss: 0.6598\n",
      "Epoch 18/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6317 - loss: 0.6443 - val_accuracy: 0.6429 - val_loss: 0.6563\n",
      "Epoch 19/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6361 - loss: 0.6441 - val_accuracy: 0.6429 - val_loss: 0.6533\n",
      "Epoch 20/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6235 - loss: 0.6496 - val_accuracy: 0.6429 - val_loss: 0.6515\n",
      "Epoch 21/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.6525 - loss: 0.6220 - val_accuracy: 0.6429 - val_loss: 0.6480\n",
      "Epoch 22/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6386 - loss: 0.6344 - val_accuracy: 0.6429 - val_loss: 0.6435\n",
      "Epoch 23/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6417 - loss: 0.6293 - val_accuracy: 0.6429 - val_loss: 0.6400\n",
      "Epoch 24/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6593 - loss: 0.6116 - val_accuracy: 0.6429 - val_loss: 0.6362\n",
      "Epoch 25/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6516 - loss: 0.6160 - val_accuracy: 0.6429 - val_loss: 0.6307\n",
      "Epoch 26/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.6390 - loss: 0.6202 - val_accuracy: 0.6429 - val_loss: 0.6290\n",
      "Epoch 27/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6439 - loss: 0.6275 - val_accuracy: 0.6429 - val_loss: 0.6277\n",
      "Epoch 28/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.6545 - loss: 0.6038 - val_accuracy: 0.6429 - val_loss: 0.6234\n",
      "Epoch 29/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6198 - loss: 0.6262 - val_accuracy: 0.6429 - val_loss: 0.6199\n",
      "Epoch 30/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6392 - loss: 0.6150 - val_accuracy: 0.6429 - val_loss: 0.6193\n",
      "Epoch 31/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.6548 - loss: 0.6015 - val_accuracy: 0.6429 - val_loss: 0.6164\n",
      "Epoch 32/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.6525 - loss: 0.5940 - val_accuracy: 0.6429 - val_loss: 0.6142\n",
      "Epoch 33/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.6579 - loss: 0.6056 - val_accuracy: 0.6429 - val_loss: 0.6126\n",
      "Epoch 34/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.6475 - loss: 0.6093 - val_accuracy: 0.6429 - val_loss: 0.6130\n",
      "Epoch 35/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6481 - loss: 0.6016 - val_accuracy: 0.6429 - val_loss: 0.6085\n",
      "Epoch 36/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6643 - loss: 0.5802 - val_accuracy: 0.6429 - val_loss: 0.6084\n",
      "Epoch 37/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6206 - loss: 0.6155 - val_accuracy: 0.6429 - val_loss: 0.6111\n",
      "Epoch 38/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6348 - loss: 0.6121 - val_accuracy: 0.6429 - val_loss: 0.6091\n",
      "Epoch 39/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6475 - loss: 0.5990 - val_accuracy: 0.6429 - val_loss: 0.6086\n",
      "Epoch 40/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.6477 - loss: 0.5875 - val_accuracy: 0.6429 - val_loss: 0.6077\n",
      "Epoch 41/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.6625 - loss: 0.5845 - val_accuracy: 0.6429 - val_loss: 0.6033\n",
      "Epoch 42/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.6560 - loss: 0.5977 - val_accuracy: 0.6429 - val_loss: 0.6069\n",
      "Epoch 43/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.6549 - loss: 0.5896 - val_accuracy: 0.6429 - val_loss: 0.6033\n",
      "Epoch 44/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6630 - loss: 0.5841 - val_accuracy: 0.6429 - val_loss: 0.6024\n",
      "Epoch 45/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6590 - loss: 0.5936 - val_accuracy: 0.6429 - val_loss: 0.6036\n",
      "Epoch 46/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.6437 - loss: 0.5934 - val_accuracy: 0.6429 - val_loss: 0.6017\n",
      "Epoch 47/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.6419 - loss: 0.5945 - val_accuracy: 0.6429 - val_loss: 0.6025\n",
      "Epoch 48/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6621 - loss: 0.5923 - val_accuracy: 0.6494 - val_loss: 0.6000\n",
      "Epoch 49/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.6702 - loss: 0.5717 - val_accuracy: 0.6429 - val_loss: 0.5990\n",
      "Epoch 50/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6550 - loss: 0.5764 - val_accuracy: 0.6364 - val_loss: 0.5996\n",
      "Epoch 51/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.6491 - loss: 0.5932 - val_accuracy: 0.6688 - val_loss: 0.6007\n",
      "Epoch 52/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.6832 - loss: 0.5748 - val_accuracy: 0.6558 - val_loss: 0.6001\n",
      "Epoch 53/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6817 - loss: 0.5933 - val_accuracy: 0.6623 - val_loss: 0.6019\n",
      "Epoch 54/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.6880 - loss: 0.5593 - val_accuracy: 0.6688 - val_loss: 0.5998\n",
      "Epoch 55/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6814 - loss: 0.5731 - val_accuracy: 0.6753 - val_loss: 0.6001\n",
      "Epoch 56/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.6702 - loss: 0.5739 - val_accuracy: 0.6623 - val_loss: 0.5963\n",
      "Epoch 57/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6713 - loss: 0.5950 - val_accuracy: 0.6623 - val_loss: 0.6021\n",
      "Epoch 58/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6651 - loss: 0.5740 - val_accuracy: 0.6558 - val_loss: 0.6004\n",
      "Epoch 59/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.6565 - loss: 0.5923 - val_accuracy: 0.6558 - val_loss: 0.6002\n",
      "Epoch 60/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6787 - loss: 0.5759 - val_accuracy: 0.6688 - val_loss: 0.6030\n",
      "Epoch 61/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.6722 - loss: 0.5941 - val_accuracy: 0.6688 - val_loss: 0.5969\n",
      "Epoch 62/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6639 - loss: 0.5948 - val_accuracy: 0.6558 - val_loss: 0.6005\n",
      "Epoch 63/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6753 - loss: 0.5665 - val_accuracy: 0.6623 - val_loss: 0.6002\n",
      "Epoch 64/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6760 - loss: 0.5832 - val_accuracy: 0.6753 - val_loss: 0.5985\n",
      "Epoch 65/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.6645 - loss: 0.5802 - val_accuracy: 0.6688 - val_loss: 0.5991\n",
      "Epoch 66/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6702 - loss: 0.5913 - val_accuracy: 0.6753 - val_loss: 0.5997\n",
      "Epoch 67/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.6872 - loss: 0.5786 - val_accuracy: 0.6818 - val_loss: 0.5940\n",
      "Epoch 68/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.6984 - loss: 0.5841 - val_accuracy: 0.6753 - val_loss: 0.5998\n",
      "Epoch 69/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6762 - loss: 0.5764 - val_accuracy: 0.6818 - val_loss: 0.5969\n",
      "Epoch 70/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6713 - loss: 0.5681 - val_accuracy: 0.6818 - val_loss: 0.5987\n",
      "Epoch 71/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.6885 - loss: 0.5931 - val_accuracy: 0.6688 - val_loss: 0.5990\n",
      "Epoch 72/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.6774 - loss: 0.5722 - val_accuracy: 0.6623 - val_loss: 0.6040\n",
      "Epoch 73/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.6954 - loss: 0.5755 - val_accuracy: 0.6623 - val_loss: 0.5909\n",
      "Epoch 74/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6912 - loss: 0.5687 - val_accuracy: 0.6688 - val_loss: 0.5972\n",
      "Epoch 75/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.6969 - loss: 0.5611 - val_accuracy: 0.6818 - val_loss: 0.6007\n",
      "Epoch 76/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7133 - loss: 0.5765 - val_accuracy: 0.6753 - val_loss: 0.5954\n",
      "Epoch 77/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7230 - loss: 0.5437 - val_accuracy: 0.6558 - val_loss: 0.6023\n",
      "Epoch 78/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6838 - loss: 0.5831 - val_accuracy: 0.6623 - val_loss: 0.6019\n",
      "Epoch 79/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6677 - loss: 0.5788 - val_accuracy: 0.6753 - val_loss: 0.6006\n",
      "Epoch 80/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7128 - loss: 0.5571 - val_accuracy: 0.6623 - val_loss: 0.6023\n",
      "Epoch 81/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7175 - loss: 0.5690 - val_accuracy: 0.6753 - val_loss: 0.5979\n",
      "Epoch 82/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7147 - loss: 0.5552 - val_accuracy: 0.6753 - val_loss: 0.6016\n",
      "Epoch 83/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7144 - loss: 0.5550 - val_accuracy: 0.6753 - val_loss: 0.5952\n",
      "Epoch 84/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7131 - loss: 0.5487 - val_accuracy: 0.6753 - val_loss: 0.5997\n",
      "Epoch 85/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7046 - loss: 0.5454 - val_accuracy: 0.6818 - val_loss: 0.6016\n",
      "Epoch 86/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7012 - loss: 0.5582 - val_accuracy: 0.6623 - val_loss: 0.5922\n",
      "Epoch 87/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6810 - loss: 0.5670 - val_accuracy: 0.6753 - val_loss: 0.5969\n",
      "Epoch 88/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.6826 - loss: 0.5867 - val_accuracy: 0.6753 - val_loss: 0.6024\n",
      "Epoch 89/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7146 - loss: 0.5728 - val_accuracy: 0.6753 - val_loss: 0.5927\n",
      "Epoch 90/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7302 - loss: 0.5477 - val_accuracy: 0.6818 - val_loss: 0.5973\n",
      "Epoch 91/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7233 - loss: 0.5649 - val_accuracy: 0.6623 - val_loss: 0.5959\n",
      "Epoch 92/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7635 - loss: 0.5235 - val_accuracy: 0.6818 - val_loss: 0.5970\n",
      "Epoch 93/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7430 - loss: 0.5641 - val_accuracy: 0.6753 - val_loss: 0.5933\n",
      "Epoch 94/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7204 - loss: 0.5672 - val_accuracy: 0.6753 - val_loss: 0.5977\n",
      "Epoch 95/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7307 - loss: 0.5574 - val_accuracy: 0.6818 - val_loss: 0.5961\n",
      "Epoch 96/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7463 - loss: 0.5539 - val_accuracy: 0.6818 - val_loss: 0.5929\n",
      "Epoch 97/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7408 - loss: 0.5541 - val_accuracy: 0.6883 - val_loss: 0.5989\n",
      "Epoch 98/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7374 - loss: 0.5477 - val_accuracy: 0.6753 - val_loss: 0.5965\n",
      "Epoch 99/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7379 - loss: 0.5419 - val_accuracy: 0.6818 - val_loss: 0.5856\n",
      "Epoch 100/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7320 - loss: 0.5387 - val_accuracy: 0.6688 - val_loss: 0.5899\n",
      "Epoch 101/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7529 - loss: 0.5378 - val_accuracy: 0.6818 - val_loss: 0.5966\n",
      "Epoch 102/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7398 - loss: 0.5466 - val_accuracy: 0.6753 - val_loss: 0.5928\n",
      "Epoch 103/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7318 - loss: 0.5480 - val_accuracy: 0.6818 - val_loss: 0.5940\n",
      "Epoch 104/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7377 - loss: 0.5475 - val_accuracy: 0.6818 - val_loss: 0.5950\n",
      "Epoch 105/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7544 - loss: 0.5423 - val_accuracy: 0.6883 - val_loss: 0.5867\n",
      "Epoch 106/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7314 - loss: 0.5497 - val_accuracy: 0.6818 - val_loss: 0.5910\n",
      "Epoch 107/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7344 - loss: 0.5744 - val_accuracy: 0.6753 - val_loss: 0.5933\n",
      "Epoch 108/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7531 - loss: 0.5205 - val_accuracy: 0.6753 - val_loss: 0.5905\n",
      "Epoch 109/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7241 - loss: 0.5462 - val_accuracy: 0.6688 - val_loss: 0.5916\n",
      "Epoch 110/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7308 - loss: 0.5592 - val_accuracy: 0.6818 - val_loss: 0.5933\n",
      "Epoch 111/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7404 - loss: 0.5478 - val_accuracy: 0.6883 - val_loss: 0.5837\n",
      "Epoch 112/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7355 - loss: 0.5643 - val_accuracy: 0.6818 - val_loss: 0.5891\n",
      "Epoch 113/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7268 - loss: 0.5555 - val_accuracy: 0.6883 - val_loss: 0.5917\n",
      "Epoch 114/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7346 - loss: 0.5616 - val_accuracy: 0.6753 - val_loss: 0.5912\n",
      "Epoch 115/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7364 - loss: 0.5474 - val_accuracy: 0.6753 - val_loss: 0.5889\n",
      "Epoch 116/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7383 - loss: 0.5502 - val_accuracy: 0.6818 - val_loss: 0.5835\n",
      "Epoch 117/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7377 - loss: 0.5271 - val_accuracy: 0.6688 - val_loss: 0.5922\n",
      "Epoch 118/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7469 - loss: 0.5297 - val_accuracy: 0.6818 - val_loss: 0.5881\n",
      "Epoch 119/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7483 - loss: 0.5452 - val_accuracy: 0.6818 - val_loss: 0.5855\n",
      "Epoch 120/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7478 - loss: 0.5296 - val_accuracy: 0.6753 - val_loss: 0.5877\n",
      "Epoch 121/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7406 - loss: 0.5423 - val_accuracy: 0.6818 - val_loss: 0.5862\n",
      "Epoch 122/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7738 - loss: 0.5110 - val_accuracy: 0.6818 - val_loss: 0.5856\n",
      "Epoch 123/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7542 - loss: 0.5372 - val_accuracy: 0.6883 - val_loss: 0.5865\n",
      "Epoch 124/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7536 - loss: 0.5389 - val_accuracy: 0.6818 - val_loss: 0.5887\n",
      "Epoch 125/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7441 - loss: 0.5356 - val_accuracy: 0.6818 - val_loss: 0.5859\n",
      "Epoch 126/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7342 - loss: 0.5485 - val_accuracy: 0.6883 - val_loss: 0.5879\n",
      "Epoch 127/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7238 - loss: 0.5578 - val_accuracy: 0.6753 - val_loss: 0.5886\n",
      "Epoch 128/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7571 - loss: 0.5217 - val_accuracy: 0.6753 - val_loss: 0.5886\n",
      "Epoch 129/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7435 - loss: 0.5318 - val_accuracy: 0.6688 - val_loss: 0.5957\n",
      "Epoch 130/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7357 - loss: 0.5408 - val_accuracy: 0.6883 - val_loss: 0.5939\n",
      "Epoch 131/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7526 - loss: 0.5150 - val_accuracy: 0.6883 - val_loss: 0.5873\n",
      "Epoch 132/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7252 - loss: 0.5400 - val_accuracy: 0.6948 - val_loss: 0.5880\n",
      "Epoch 133/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7197 - loss: 0.5506 - val_accuracy: 0.7013 - val_loss: 0.5890\n",
      "Epoch 134/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7106 - loss: 0.5499 - val_accuracy: 0.7143 - val_loss: 0.5829\n",
      "Epoch 135/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7611 - loss: 0.5183 - val_accuracy: 0.6883 - val_loss: 0.5936\n",
      "Epoch 136/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7380 - loss: 0.5464 - val_accuracy: 0.6883 - val_loss: 0.5891\n",
      "Epoch 137/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7350 - loss: 0.5458 - val_accuracy: 0.6948 - val_loss: 0.5869\n",
      "Epoch 138/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7474 - loss: 0.5314 - val_accuracy: 0.6948 - val_loss: 0.5894\n",
      "Epoch 139/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7309 - loss: 0.5506 - val_accuracy: 0.6948 - val_loss: 0.5905\n",
      "Epoch 140/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7333 - loss: 0.5399 - val_accuracy: 0.7078 - val_loss: 0.5836\n",
      "Epoch 141/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7133 - loss: 0.5303 - val_accuracy: 0.7013 - val_loss: 0.5882\n",
      "Epoch 142/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7514 - loss: 0.5110 - val_accuracy: 0.7208 - val_loss: 0.5868\n",
      "Epoch 143/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7432 - loss: 0.5277 - val_accuracy: 0.7078 - val_loss: 0.5922\n",
      "Epoch 144/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7439 - loss: 0.5281 - val_accuracy: 0.7078 - val_loss: 0.5869\n",
      "Epoch 145/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7460 - loss: 0.5313 - val_accuracy: 0.7143 - val_loss: 0.5838\n",
      "Epoch 146/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7515 - loss: 0.5358 - val_accuracy: 0.7208 - val_loss: 0.5890\n",
      "Epoch 147/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7326 - loss: 0.5414 - val_accuracy: 0.7013 - val_loss: 0.5891\n",
      "Epoch 148/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7291 - loss: 0.5375 - val_accuracy: 0.7143 - val_loss: 0.5885\n",
      "Epoch 149/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7509 - loss: 0.5365 - val_accuracy: 0.7143 - val_loss: 0.5917\n",
      "Epoch 150/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7396 - loss: 0.5258 - val_accuracy: 0.7208 - val_loss: 0.5842\n",
      "Epoch 151/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7426 - loss: 0.5289 - val_accuracy: 0.7143 - val_loss: 0.5891\n",
      "Epoch 152/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7389 - loss: 0.5240 - val_accuracy: 0.7143 - val_loss: 0.5904\n",
      "Epoch 153/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7495 - loss: 0.5328 - val_accuracy: 0.7143 - val_loss: 0.5882\n",
      "Epoch 154/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7576 - loss: 0.5311 - val_accuracy: 0.7013 - val_loss: 0.5929\n",
      "Epoch 155/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7339 - loss: 0.5396 - val_accuracy: 0.7208 - val_loss: 0.5876\n",
      "Epoch 156/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7398 - loss: 0.5355 - val_accuracy: 0.7208 - val_loss: 0.5928\n",
      "Epoch 157/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7341 - loss: 0.5315 - val_accuracy: 0.7208 - val_loss: 0.5892\n",
      "Epoch 158/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7629 - loss: 0.5166 - val_accuracy: 0.7208 - val_loss: 0.5858\n",
      "Epoch 159/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7429 - loss: 0.5346 - val_accuracy: 0.7273 - val_loss: 0.5881\n",
      "Epoch 160/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7411 - loss: 0.5241 - val_accuracy: 0.7143 - val_loss: 0.5918\n",
      "Epoch 161/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7251 - loss: 0.5350 - val_accuracy: 0.7013 - val_loss: 0.5893\n",
      "Epoch 162/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7541 - loss: 0.5180 - val_accuracy: 0.7208 - val_loss: 0.5896\n",
      "Epoch 163/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7416 - loss: 0.5254 - val_accuracy: 0.7273 - val_loss: 0.5865\n",
      "Epoch 164/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7499 - loss: 0.5220 - val_accuracy: 0.7143 - val_loss: 0.5874\n",
      "Epoch 165/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7299 - loss: 0.5448 - val_accuracy: 0.6948 - val_loss: 0.5941\n",
      "Epoch 166/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7644 - loss: 0.5050 - val_accuracy: 0.7143 - val_loss: 0.5964\n",
      "Epoch 167/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7361 - loss: 0.5177 - val_accuracy: 0.7208 - val_loss: 0.5892\n",
      "Epoch 168/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7316 - loss: 0.5459 - val_accuracy: 0.7338 - val_loss: 0.5932\n",
      "Epoch 169/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7539 - loss: 0.5183 - val_accuracy: 0.7273 - val_loss: 0.5889\n",
      "Epoch 170/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7259 - loss: 0.5324 - val_accuracy: 0.7208 - val_loss: 0.5936\n",
      "Epoch 171/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7651 - loss: 0.5176 - val_accuracy: 0.7208 - val_loss: 0.5931\n",
      "Epoch 172/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7613 - loss: 0.5188 - val_accuracy: 0.7208 - val_loss: 0.5921\n",
      "Epoch 173/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7382 - loss: 0.5183 - val_accuracy: 0.7338 - val_loss: 0.5933\n",
      "Epoch 174/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7601 - loss: 0.5215 - val_accuracy: 0.7143 - val_loss: 0.6001\n",
      "Epoch 175/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7505 - loss: 0.5078 - val_accuracy: 0.7338 - val_loss: 0.5927\n",
      "Epoch 176/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7455 - loss: 0.5266 - val_accuracy: 0.7338 - val_loss: 0.5891\n",
      "Epoch 177/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7369 - loss: 0.5300 - val_accuracy: 0.7143 - val_loss: 0.5931\n",
      "Epoch 178/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7439 - loss: 0.5171 - val_accuracy: 0.7208 - val_loss: 0.5993\n",
      "Epoch 179/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7386 - loss: 0.5255 - val_accuracy: 0.7013 - val_loss: 0.5954\n",
      "Epoch 180/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7456 - loss: 0.5064 - val_accuracy: 0.7338 - val_loss: 0.5921\n",
      "Epoch 181/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7593 - loss: 0.5202 - val_accuracy: 0.7143 - val_loss: 0.5935\n",
      "Epoch 182/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7451 - loss: 0.5212 - val_accuracy: 0.7403 - val_loss: 0.5944\n",
      "Epoch 183/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7482 - loss: 0.5361 - val_accuracy: 0.7273 - val_loss: 0.5924\n",
      "Epoch 184/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7416 - loss: 0.5237 - val_accuracy: 0.7208 - val_loss: 0.5915\n",
      "Epoch 185/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7531 - loss: 0.5269 - val_accuracy: 0.7403 - val_loss: 0.5938\n",
      "Epoch 186/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7291 - loss: 0.5367 - val_accuracy: 0.7338 - val_loss: 0.5934\n",
      "Epoch 187/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7405 - loss: 0.5295 - val_accuracy: 0.7273 - val_loss: 0.5928\n",
      "Epoch 188/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7484 - loss: 0.5170 - val_accuracy: 0.7273 - val_loss: 0.5938\n",
      "Epoch 189/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7560 - loss: 0.5291 - val_accuracy: 0.7403 - val_loss: 0.5931\n",
      "Epoch 190/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7488 - loss: 0.5251 - val_accuracy: 0.7468 - val_loss: 0.5903\n",
      "Epoch 191/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7708 - loss: 0.4945 - val_accuracy: 0.7208 - val_loss: 0.5920\n",
      "Epoch 192/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7664 - loss: 0.5077 - val_accuracy: 0.7208 - val_loss: 0.5881\n",
      "Epoch 193/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7491 - loss: 0.5243 - val_accuracy: 0.7078 - val_loss: 0.5953\n",
      "Epoch 194/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7254 - loss: 0.5322 - val_accuracy: 0.7273 - val_loss: 0.5940\n",
      "Epoch 195/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7621 - loss: 0.5116 - val_accuracy: 0.7403 - val_loss: 0.5839\n",
      "Epoch 196/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7689 - loss: 0.5219 - val_accuracy: 0.7338 - val_loss: 0.5915\n",
      "Epoch 197/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7537 - loss: 0.5190 - val_accuracy: 0.7338 - val_loss: 0.5961\n",
      "Epoch 198/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7671 - loss: 0.5026 - val_accuracy: 0.7403 - val_loss: 0.5839\n",
      "Epoch 199/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7591 - loss: 0.5087 - val_accuracy: 0.7273 - val_loss: 0.5924\n",
      "Epoch 200/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7553 - loss: 0.5176 - val_accuracy: 0.7338 - val_loss: 0.5921\n",
      "Epoch 201/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7891 - loss: 0.4775 - val_accuracy: 0.7468 - val_loss: 0.5881\n",
      "Epoch 202/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7520 - loss: 0.5228 - val_accuracy: 0.7338 - val_loss: 0.5899\n",
      "Epoch 203/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7462 - loss: 0.5303 - val_accuracy: 0.7273 - val_loss: 0.5923\n",
      "Epoch 204/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7559 - loss: 0.5168 - val_accuracy: 0.7273 - val_loss: 0.5907\n",
      "Epoch 205/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7537 - loss: 0.5027 - val_accuracy: 0.7273 - val_loss: 0.5908\n",
      "Epoch 206/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7538 - loss: 0.5076 - val_accuracy: 0.7143 - val_loss: 0.5888\n",
      "Epoch 207/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7726 - loss: 0.5106 - val_accuracy: 0.7338 - val_loss: 0.5912\n",
      "Epoch 208/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7665 - loss: 0.4943 - val_accuracy: 0.7208 - val_loss: 0.5896\n",
      "Epoch 209/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7719 - loss: 0.4882 - val_accuracy: 0.7338 - val_loss: 0.5917\n",
      "Epoch 210/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7686 - loss: 0.5002 - val_accuracy: 0.7338 - val_loss: 0.5851\n",
      "Epoch 211/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7387 - loss: 0.5215 - val_accuracy: 0.7143 - val_loss: 0.5967\n",
      "Epoch 212/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7499 - loss: 0.5170 - val_accuracy: 0.7013 - val_loss: 0.5934\n",
      "Epoch 213/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7580 - loss: 0.5072 - val_accuracy: 0.7273 - val_loss: 0.5856\n",
      "Epoch 214/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7473 - loss: 0.5290 - val_accuracy: 0.7273 - val_loss: 0.5884\n",
      "Epoch 215/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7530 - loss: 0.5160 - val_accuracy: 0.7273 - val_loss: 0.5978\n",
      "Epoch 216/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7506 - loss: 0.5075 - val_accuracy: 0.7143 - val_loss: 0.5874\n",
      "Epoch 217/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7767 - loss: 0.4986 - val_accuracy: 0.7338 - val_loss: 0.5865\n",
      "Epoch 218/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7759 - loss: 0.5121 - val_accuracy: 0.7208 - val_loss: 0.5935\n",
      "Epoch 219/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7739 - loss: 0.5045 - val_accuracy: 0.7208 - val_loss: 0.5894\n",
      "Epoch 220/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7707 - loss: 0.4923 - val_accuracy: 0.7403 - val_loss: 0.5888\n",
      "Epoch 221/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7332 - loss: 0.5321 - val_accuracy: 0.7078 - val_loss: 0.5950\n",
      "Epoch 222/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7588 - loss: 0.5088 - val_accuracy: 0.7403 - val_loss: 0.5921\n",
      "Epoch 223/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7457 - loss: 0.5242 - val_accuracy: 0.7338 - val_loss: 0.5928\n",
      "Epoch 224/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7796 - loss: 0.4908 - val_accuracy: 0.7273 - val_loss: 0.5897\n",
      "Epoch 225/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7552 - loss: 0.5114 - val_accuracy: 0.7273 - val_loss: 0.5912\n",
      "Epoch 226/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7501 - loss: 0.5085 - val_accuracy: 0.7338 - val_loss: 0.6006\n",
      "Epoch 227/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7414 - loss: 0.5071 - val_accuracy: 0.7338 - val_loss: 0.5856\n",
      "Epoch 228/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7796 - loss: 0.4820 - val_accuracy: 0.7143 - val_loss: 0.5938\n",
      "Epoch 229/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7345 - loss: 0.5327 - val_accuracy: 0.7338 - val_loss: 0.5969\n",
      "Epoch 230/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7530 - loss: 0.5065 - val_accuracy: 0.7273 - val_loss: 0.5942\n",
      "Epoch 231/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7493 - loss: 0.5187 - val_accuracy: 0.7273 - val_loss: 0.5979\n",
      "Epoch 232/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7378 - loss: 0.5185 - val_accuracy: 0.7208 - val_loss: 0.5956\n",
      "Epoch 233/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7731 - loss: 0.4899 - val_accuracy: 0.7208 - val_loss: 0.5950\n",
      "Epoch 234/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7549 - loss: 0.5054 - val_accuracy: 0.7273 - val_loss: 0.5970\n",
      "Epoch 235/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7568 - loss: 0.4796 - val_accuracy: 0.7208 - val_loss: 0.5885\n",
      "Epoch 236/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7491 - loss: 0.5079 - val_accuracy: 0.7078 - val_loss: 0.5949\n",
      "Epoch 237/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7502 - loss: 0.5135 - val_accuracy: 0.7208 - val_loss: 0.6002\n",
      "Epoch 238/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7618 - loss: 0.5049 - val_accuracy: 0.7208 - val_loss: 0.5832\n",
      "Epoch 239/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7657 - loss: 0.4907 - val_accuracy: 0.7273 - val_loss: 0.5942\n",
      "Epoch 240/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7976 - loss: 0.4634 - val_accuracy: 0.7078 - val_loss: 0.6037\n",
      "Epoch 241/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7540 - loss: 0.5242 - val_accuracy: 0.7143 - val_loss: 0.6016\n",
      "Epoch 242/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7822 - loss: 0.4990 - val_accuracy: 0.7273 - val_loss: 0.5985\n",
      "Epoch 243/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7697 - loss: 0.4812 - val_accuracy: 0.7143 - val_loss: 0.5975\n",
      "Epoch 244/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7532 - loss: 0.5390 - val_accuracy: 0.7273 - val_loss: 0.5989\n",
      "Epoch 245/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7546 - loss: 0.5030 - val_accuracy: 0.7338 - val_loss: 0.5916\n",
      "Epoch 246/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7567 - loss: 0.5128 - val_accuracy: 0.7143 - val_loss: 0.6043\n",
      "Epoch 247/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7576 - loss: 0.4900 - val_accuracy: 0.7078 - val_loss: 0.5931\n",
      "Epoch 248/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7654 - loss: 0.5022 - val_accuracy: 0.7208 - val_loss: 0.5976\n",
      "Epoch 249/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7754 - loss: 0.4975 - val_accuracy: 0.7078 - val_loss: 0.6022\n",
      "Epoch 250/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7424 - loss: 0.5157 - val_accuracy: 0.7338 - val_loss: 0.5912\n",
      "Epoch 251/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7696 - loss: 0.4898 - val_accuracy: 0.7208 - val_loss: 0.6035\n",
      "Epoch 252/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7595 - loss: 0.5016 - val_accuracy: 0.7338 - val_loss: 0.5921\n",
      "Epoch 253/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7817 - loss: 0.4875 - val_accuracy: 0.7143 - val_loss: 0.6041\n",
      "Epoch 254/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7595 - loss: 0.5050 - val_accuracy: 0.7273 - val_loss: 0.5942\n",
      "Epoch 255/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7335 - loss: 0.5205 - val_accuracy: 0.7013 - val_loss: 0.6048\n",
      "Epoch 256/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7636 - loss: 0.5080 - val_accuracy: 0.7273 - val_loss: 0.5916\n",
      "Epoch 257/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7620 - loss: 0.5104 - val_accuracy: 0.7208 - val_loss: 0.6014\n",
      "Epoch 258/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7681 - loss: 0.5023 - val_accuracy: 0.7208 - val_loss: 0.6028\n",
      "Epoch 259/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7497 - loss: 0.5146 - val_accuracy: 0.7143 - val_loss: 0.5849\n",
      "Epoch 260/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7744 - loss: 0.5052 - val_accuracy: 0.7143 - val_loss: 0.6103\n",
      "Epoch 261/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7694 - loss: 0.4713 - val_accuracy: 0.7273 - val_loss: 0.5940\n",
      "Epoch 262/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7660 - loss: 0.4954 - val_accuracy: 0.7273 - val_loss: 0.5934\n",
      "Epoch 263/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7653 - loss: 0.5031 - val_accuracy: 0.7273 - val_loss: 0.5943\n",
      "Epoch 264/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7657 - loss: 0.4842 - val_accuracy: 0.6948 - val_loss: 0.6025\n",
      "Epoch 265/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7849 - loss: 0.4823 - val_accuracy: 0.7208 - val_loss: 0.5927\n",
      "Epoch 266/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7812 - loss: 0.4877 - val_accuracy: 0.7143 - val_loss: 0.5970\n",
      "Epoch 267/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7734 - loss: 0.4881 - val_accuracy: 0.7143 - val_loss: 0.6023\n",
      "Epoch 268/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7628 - loss: 0.4909 - val_accuracy: 0.7273 - val_loss: 0.5920\n",
      "Epoch 269/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7699 - loss: 0.4853 - val_accuracy: 0.7013 - val_loss: 0.6059\n",
      "Epoch 270/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7856 - loss: 0.4783 - val_accuracy: 0.7208 - val_loss: 0.5981\n",
      "Epoch 271/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7558 - loss: 0.4993 - val_accuracy: 0.7208 - val_loss: 0.5946\n",
      "Epoch 272/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7746 - loss: 0.4951 - val_accuracy: 0.7273 - val_loss: 0.5948\n",
      "Epoch 273/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7617 - loss: 0.5130 - val_accuracy: 0.7013 - val_loss: 0.6019\n",
      "Epoch 274/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7797 - loss: 0.4633 - val_accuracy: 0.7208 - val_loss: 0.5957\n",
      "Epoch 275/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7605 - loss: 0.4975 - val_accuracy: 0.7078 - val_loss: 0.6023\n",
      "Epoch 276/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7750 - loss: 0.4867 - val_accuracy: 0.7338 - val_loss: 0.6006\n",
      "Epoch 277/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7364 - loss: 0.5137 - val_accuracy: 0.7143 - val_loss: 0.5994\n",
      "Epoch 278/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7799 - loss: 0.4705 - val_accuracy: 0.7273 - val_loss: 0.6002\n",
      "Epoch 279/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7488 - loss: 0.5133 - val_accuracy: 0.7013 - val_loss: 0.6061\n",
      "Epoch 280/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7476 - loss: 0.5206 - val_accuracy: 0.7273 - val_loss: 0.6005\n",
      "Epoch 281/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7534 - loss: 0.4986 - val_accuracy: 0.7208 - val_loss: 0.5982\n",
      "Epoch 282/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7791 - loss: 0.4975 - val_accuracy: 0.7208 - val_loss: 0.6024\n",
      "Epoch 283/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7779 - loss: 0.4776 - val_accuracy: 0.7208 - val_loss: 0.6015\n",
      "Epoch 284/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7534 - loss: 0.5100 - val_accuracy: 0.7403 - val_loss: 0.5948\n",
      "Epoch 285/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7722 - loss: 0.5045 - val_accuracy: 0.7208 - val_loss: 0.5974\n",
      "Epoch 286/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7921 - loss: 0.4790 - val_accuracy: 0.7208 - val_loss: 0.6036\n",
      "Epoch 287/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7648 - loss: 0.4819 - val_accuracy: 0.7273 - val_loss: 0.6047\n",
      "Epoch 288/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7660 - loss: 0.4729 - val_accuracy: 0.7338 - val_loss: 0.5993\n",
      "Epoch 289/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7624 - loss: 0.5049 - val_accuracy: 0.7208 - val_loss: 0.6060\n",
      "Epoch 290/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7526 - loss: 0.4899 - val_accuracy: 0.7208 - val_loss: 0.5976\n",
      "Epoch 291/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7626 - loss: 0.4834 - val_accuracy: 0.7143 - val_loss: 0.6016\n",
      "Epoch 292/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7712 - loss: 0.4927 - val_accuracy: 0.7143 - val_loss: 0.6071\n",
      "Epoch 293/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7799 - loss: 0.4722 - val_accuracy: 0.7208 - val_loss: 0.6010\n",
      "Epoch 294/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7701 - loss: 0.4821 - val_accuracy: 0.7403 - val_loss: 0.6002\n",
      "Epoch 295/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7327 - loss: 0.5270 - val_accuracy: 0.7143 - val_loss: 0.6075\n",
      "Epoch 296/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7593 - loss: 0.4974 - val_accuracy: 0.7338 - val_loss: 0.5957\n",
      "Epoch 297/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7652 - loss: 0.4936 - val_accuracy: 0.7143 - val_loss: 0.6055\n",
      "Epoch 298/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7844 - loss: 0.4834 - val_accuracy: 0.7208 - val_loss: 0.5976\n",
      "Epoch 299/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7492 - loss: 0.5012 - val_accuracy: 0.7208 - val_loss: 0.6108\n",
      "Epoch 300/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7829 - loss: 0.4797 - val_accuracy: 0.7338 - val_loss: 0.5983\n",
      "Epoch 301/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7463 - loss: 0.5020 - val_accuracy: 0.7403 - val_loss: 0.6025\n",
      "Epoch 302/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7718 - loss: 0.4815 - val_accuracy: 0.7403 - val_loss: 0.5992\n",
      "Epoch 303/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7545 - loss: 0.4993 - val_accuracy: 0.7013 - val_loss: 0.6062\n",
      "Epoch 304/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7529 - loss: 0.4951 - val_accuracy: 0.7208 - val_loss: 0.6047\n",
      "Epoch 305/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7587 - loss: 0.4875 - val_accuracy: 0.7273 - val_loss: 0.6056\n",
      "Epoch 306/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7655 - loss: 0.4949 - val_accuracy: 0.7273 - val_loss: 0.6034\n",
      "Epoch 307/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7943 - loss: 0.4883 - val_accuracy: 0.7208 - val_loss: 0.6052\n",
      "Epoch 308/400\n",
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7512 - loss: 0.4965 - val_accuracy: 0.7273 - val_loss: 0.6039\n",
      "Epoch 309/400\n",
      "\u001b[1m 7/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.7596 - loss: 0.491407"
     ]
    }
   ],
   "source": [
    "# Fit the model\n",
    "history = model.fit(X, Y, validation_split=0.20, epochs=400, batch_size=64, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# list all data in history\n",
    "print(history.history.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# summarize history for accuracy\n",
    "plt.figure(figsize=(12,8))\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()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# summarize history for loss\n",
    "plt.figure(figsize=(12,8))\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": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# evaluate the model\n",
    "scores = model.evaluate(X, Y)\n",
    "print(\"\\n{}: {}\".format(model.metrics_names[1], scores[1]*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate predictions\n",
    "predictions = model.predict(X)\n",
    "rounded = [round(x[0]) for x in predictions]\n",
    "\n",
    "for pred in list(zip(predictions[0:10], rounded[0:10])):\n",
    "    print(pred)"
   ]
  }
 ],
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