code/experimental-notebooks/7-improving-inter-session-eegnet.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "337221df",
   "metadata": {},
   "source": [
    "# Improving inter-session EEGNet\n",
    "\n",
    "In the previous notebooks `6-DL-based-classification.ipynb` it was shown that EEGNet has excellent performance for offline classification of MI EEG data of the same patient on the same session. However, once we considered inter-session data we saw that the non stationarity of the EEG data on different sessions caused issues. This is to be expected as it is a general issue in BCI applications that remains largely unsolved. Before experimenting with the even more challanging inter-subject setting, where a general BCI model is trained to work on an unseen patient, we see if improvements can be made to the inter-session performance of EEGNet through multiple strategies.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5341c6d",
   "metadata": {},
   "source": [
    "## Table of Contents\n",
    "\n",
    "- Checking requirements\n",
    "  - Correct anaconda environment\n",
    "  - Correct module access\n",
    "  - Correct file access\n",
    "  - Checking TensorFlow GPU support\n",
    "- About the data\n",
    "- Tensorboard\n",
    "- Fine-tuning EEGNet on few samples\n",
    "  - Prepping the data\n",
    "  - Training the model on the train data\n",
    "  - Testing base model performance on unseen trial\n",
    "  - Do the fine-tuning"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "292165d3",
   "metadata": {},
   "source": [
    "<hr><hr>\n",
    "\n",
    "## Checking requirements"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f55ad17",
   "metadata": {},
   "source": [
    "### Correct anaconda environment\n",
    "\n",
    "The `bci-master-thesis` anaconda environment should be active to ensure proper support. Installation instructions are available on [the GitHub repository of the BCI master thesis project](https://www.github.com/pikawika/bci-master-thesis)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "334d5fd4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Active environment: bci-master-thesis\n",
      "Correct environment: True\n",
      "\n",
      "Python version: 3.8.10\n",
      "Correct Python version: True\n"
     ]
    }
   ],
   "source": [
    "####################################################\n",
    "# CHECKING FOR RIGHT ANACONDA ENVIRONMENT\n",
    "####################################################\n",
    "\n",
    "import os\n",
    "from platform import python_version\n",
    "from pathlib import Path\n",
    "from copy import copy\n",
    "\n",
    "print(f\"Active environment: {os.environ['CONDA_DEFAULT_ENV']}\")\n",
    "print(f\"Correct environment: {os.environ['CONDA_DEFAULT_ENV'] == 'bci-master-thesis'}\")\n",
    "print(f\"\\nPython version: {python_version()}\")\n",
    "print(f\"Correct Python version: {python_version() == '3.8.10'}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22166668",
   "metadata": {},
   "source": [
    "<hr>\n",
    "\n",
    "### Correct module access\n",
    "\n",
    "The following codeblock will load in all required modules and show if the versions match those that are recommended."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ab632204",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MNE version (1.0.2 recommended): 1.0.2\n",
      "PyRieMann version (0.2.7 recommended): 0.2.7\n",
      "Numpy version (1.21.5 recommended): 1.21.5\n",
      "Pandas version (1.4.1 recommended): 1.4.1\n",
      "Scikit-learn version (1.0.2 recommended): 1.0.2\n",
      "TensorFlow version (2.8.0 recommended): 2.8.0\n",
      "Keras version (2.8.0 recommended): 2.8.0\n",
      "Pickle version (4.0 recommended): 4.0\n",
      "Matplotlib version (3.5.1 recommended): 3.5.1\n"
     ]
    }
   ],
   "source": [
    "####################################################\n",
    "# LOADING MODULES\n",
    "####################################################\n",
    "\n",
    "# allow reloading of libraries\n",
    "import importlib\n",
    "\n",
    "# Load utils with reloading allowed\n",
    "import sys\n",
    "sys.path.append('../utils')\n",
    "import CLA_dataset\n",
    "import TF_tools\n",
    "importlib.reload(CLA_dataset)\n",
    "importlib.reload(TF_tools)\n",
    "\n",
    "# Load EEGModels\n",
    "import EEGModels\n",
    "from EEGModels import EEGNet, ShallowConvNet, DeepConvNet\n",
    "\n",
    "# IO functions\n",
    "from IPython.utils import io\n",
    "\n",
    "# Set logging level for MNE before loading MNE\n",
    "os.environ['MNE_LOGGING_LEVEL'] = 'WARNING'\n",
    "\n",
    "# Modules tailored for EEG data\n",
    "import mne; print(f\"MNE version (1.0.2 recommended): {mne.__version__}\")\n",
    "\n",
    "import pyriemann as prm; print(f\"PyRieMann version (0.2.7 recommended): {prm.__version__}\")\n",
    "\n",
    "# Data manipulation modules\n",
    "import numpy as np; print(f\"Numpy version (1.21.5 recommended): {np.__version__}\")\n",
    "import pandas as pd; print(f\"Pandas version (1.4.1 recommended): {pd.__version__}\")\n",
    "\n",
    "# ML libraries\n",
    "import sklearn;  print(f\"Scikit-learn version (1.0.2 recommended): {sklearn.__version__}\")\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import ConfusionMatrixDisplay, accuracy_score\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "\n",
    "# Deep Learning libraries\n",
    "import tensorflow as tf;  print(f\"TensorFlow version (2.8.0 recommended): {tf.__version__}\")\n",
    "\n",
    "import keras; print(f\"Keras version (2.8.0 recommended): {keras.__version__}\")\n",
    "from keras.callbacks import ModelCheckpoint\n",
    "\n",
    "# Storing files\n",
    "import pickle;  print(f\"Pickle version (4.0 recommended): {pickle.format_version}\")\n",
    "\n",
    "# Plotting\n",
    "import matplotlib; print(f\"Matplotlib version (3.5.1 recommended): {matplotlib.__version__}\")\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "813bb5de",
   "metadata": {},
   "source": [
    "<hr>\n",
    "\n",
    "### Correct file access\n",
    "\n",
    "As mentioned, this experimental notebook uses a database provided by [Kaya et al](https://doi.org/10.1038/sdata.2018.211). The CLA dataset in particular. Instructions on where to get the data are available on [the GitHub repository of the BCI master thesis project](https://www.github.com/pikawika/bci-master-thesis). These instructions are under `bci-master-thesis/code/data/CLA/README.md`. FIF files from this same dataset are also made available in [the GitHub repository of the BCI master thesis project](https://www.github.com/pikawika/bci-master-thesis). A check on the availability of these two datasets is performed below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "caa1d182",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Full Matlab CLA file access: True\n",
      "Full MNE CLA file access: True\n"
     ]
    }
   ],
   "source": [
    "####################################################\n",
    "# CHECKING FILE ACCESS\n",
    "####################################################\n",
    "\n",
    "# Use util to determine if we have access\n",
    "print(\"Full Matlab CLA file access: \" + str(CLA_dataset.check_matlab_files_availability()))\n",
    "print(\"Full MNE CLA file access: \" + str(CLA_dataset.check_mne_files_availability()))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43a71dde",
   "metadata": {},
   "source": [
    "<hr>\n",
    "\n",
    "### Checking TensorFlow GPU support\n",
    "\n",
    "If you want to use TensorFlow with GPU acceleration, the below codeblock can help you gather insight."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8d8d50f9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 1 CPUs available under the names:\n",
      "[PhysicalDevice(name='/physical_device:CPU:0', device_type='CPU')]\n",
      "\n",
      "\n",
      "There are not GPUs available.\n"
     ]
    }
   ],
   "source": [
    "TF_tools.check_tf_cpu_gpu_presence()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1629ba2",
   "metadata": {},
   "source": [
    "<hr><hr>\n",
    "\n",
    "## About the data\n",
    "\n",
    "Remember the meaning of the markers:\n",
    "- 0: “blank” or nothing is displayed in eGUI\n",
    "    - Can be seen as a break between stimuli, thus random EEG data that should probably be ignored\n",
    "- 1: Left hand action\n",
    "    - EEG data for MI of the left hand\n",
    "- 2: Right hand action\n",
    "    - EEG data for MI of the right hand\n",
    "- 3: Passive/neutral\n",
    "    - EEG data for MI of neither left nor right hand but 'focused'\n",
    "- 91: inter-session rest break period\n",
    "- 92: experiment end\n",
    "- 99: initial relaxation period"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1960330e",
   "metadata": {},
   "source": [
    "<hr> <hr>\n",
    "\n",
    "## Tensorboard\n",
    "\n",
    "To launch the tensorboard use the following command in the `experimental-notebooks` folder:\n",
    "- Windows: `tensorboard --logdir=./logs/`\n",
    "- MacOS: `tensorboard --logdir='./logs/'`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1b35576",
   "metadata": {},
   "source": [
    "<hr><hr>\n",
    "\n",
    "## Fine-tuning EEGNet on few samples\n",
    "\n",
    "Since we want to improve the results on a new trial, it is not unreasonable to think that we want to recalibrate the new model.\n",
    "In it's simplest form, recalibration can be done by taking the previously trained model and training it further on new data from the new trial.\n",
    "This new data may consists of only a small amount of samples as to not become annoying for the user.\n",
    "Because we are fine-tuning the model for the new trial rather then completely retraining it, the learning rate is often lowered to combat overfitting to the few samples at hand.\n",
    "In our case, we consider that we have access to the first 50 samples of the new trial for recalibration as this corresponds roughly to roughly 5 minutes of user input.\n",
    "\n",
    "In this experiment we compare our approach with the previous obtained results to see if we have improved it.\n",
    "\n",
    "**Remember the results for subject `C`**\n",
    "\n",
    "| **Test index** | **Train index** | **Same-session EEGNet** | **Inter-session EEGNet** | **Fine-tuned EEGNet** |\n",
    "|----------------|-----------------|-------------------------|--------------------------|-----------------------|\n",
    "| 0              | 1 + 2           | 0.944 / 0.934           | 0.916 / 0.920            | 0.xxx / 0.xxx         |\n",
    "| 1              | 0 + 2           | 0.931 / 0.913           | 0.852 / 0.860            | 0.xxx / 0.xxx         |\n",
    "| 2              | 1 + 2           | 0.892 / 0.878           | 0.607 / 0.594            | 0.687 / 0.618         |\n",
    "| 0              | 1               | 0.944 / 0.934           | 0.891 / 0.860            | 0.xxx / 0.xxx         |\n",
    "| 1              | 2               | 0.931 / 0.913           | 0.794 / 0.783            | 0.xxx / 0.xxx         |\n",
    "\n",
    "**For subject `B` these results are**\n",
    "\n",
    "| **Test index** | **Train index** | **Same-session EEGNet** | **Inter-session EEGNet** | **Fine-tuned EEGNet** |\n",
    "|----------------|-----------------|-------------------------|--------------------------|-----------------------|\n",
    "| 0              | 1 + 2           | 0.569 / 0.587           | 0.498 / 0.489            | 0.xxx / 0.xxx         |\n",
    "| 1              | 0 + 2           | 0.701 / 0.684           | 0.640 / 0.654            | 0.xxx / 0.xxx         |\n",
    "| 2              | 1 + 2           | 0.816 / 0.792           | 0.677 / 0.660            | 0.xxx / 0.xxx         |\n",
    "\n",
    "**For subject `E` the results are**\n",
    "\n",
    "| **Test index** | **Train index** | **Same-session EEGNet** | **Inter-session EEGNet** | **Fine-tuned EEGNet** |\n",
    "|----------------|-----------------|-------------------------|--------------------------|-----------------------|\n",
    "| 0              | 1 + 2           | 0.792 / 0.785           | 0.714 / 0.713            | 0.xxx / 0.xxx         |\n",
    "| 1              | 0 + 2           | 0.795 / 0.799           | 0.667 / 0.646            | 0.xxx / 0.xxx         |\n",
    "| 2              | 1 + 2           | 0.864 / 0.864           | 0.734 / 0.688            | 0.xxx / 0.xxx         |\n",
    "\n",
    "Again, we see that the DL approach using EEGNet far outperforms the CSP approach."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56567b3c",
   "metadata": {},
   "source": [
    "### Prepping the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "757ebb42",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading 0 ... 666799  =      0.000 ...  3333.995 secs...\n",
      "Reading 0 ... 681199  =      0.000 ...  3405.995 secs...\n",
      "Reading 0 ... 669399  =      0.000 ...  3346.995 secs...\n",
      "\n",
      "Loaded test epochs and extracted labels as well as new finetune test samples\n",
      "Loaded train epochs and extracted labels\n",
      "Loaded train epochs and extracted labels\n",
      "\n",
      "\n",
      "Concatenated the train epochs\n",
      "Total amount of train labels: 1919\n",
      "\n",
      "\n",
      "Got the test epochs\n",
      "Total amount of test labels: 930\n",
      "\n",
      "Train labels OHE match regular labels: True\n",
      "Test labels OHE match regular labels: True\n",
      "Finetune labels OHE match regular labels: True\n",
      "\n",
      "\n",
      "Got the finetune test samples epochs\n",
      "Total amount of finetune labels: 30\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"table table-hover table-striped table-sm table-responsive small\">\n",
       "    <tr>\n",
       "        <th>Number of events</th>\n",
       "        <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th>Events</th>\n",
       "        \n",
       "        <td>task/left: 10<br/>task/neutral: 10<br/>task/right: 10</td>\n",
       "        \n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th>Time range</th>\n",
       "        <td>-1.500 – 2.500 sec</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th>Baseline</th>\n",
       "        <td>0.000 – 1.000 sec</td>\n",
       "    </tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Epochs |  30 events (good & bad), -1.5 - 2.5 sec, baseline 0 – 1 sec, ~37 kB, data not loaded,\n",
       " 'task/left': 10\n",
       " 'task/right': 10\n",
       " 'task/neutral': 10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "####################################################\n",
    "# PREPPING THE DATA\n",
    "####################################################\n",
    "\n",
    "# Specify params\n",
    "test_subject = \"C\"\n",
    "test_idx = 0\n",
    "\n",
    "# Get data and choose test trial\n",
    "mne_raws = CLA_dataset.get_all_raw_mne_data_for_subject(subject_id= test_subject)\n",
    "test_trial = mne_raws[test_idx]\n",
    "\n",
    "# Init variables\n",
    "epochs = []\n",
    "labels_train = []\n",
    "\n",
    "print()\n",
    "\n",
    "for single_mne_raw in mne_raws:\n",
    "    # Load epochs from raw\n",
    "    mne_fixed_window_epochs = CLA_dataset.get_usefull_epochs_from_raw(single_mne_raw, start_offset=-1.5, end_offset=1.5)['task/neutral', 'task/left', 'task/right']\n",
    "    mne_fixed_window_epochs.reset_drop_log_selection()\n",
    "    \n",
    "    if (single_mne_raw == test_trial):\n",
    "        # Get 30 samples from test set for further calibration\n",
    "        new_test_samples = np.hstack([mne_fixed_window_epochs['task/neutral'][0:10].selection,\n",
    "                                      mne_fixed_window_epochs['task/left'][0:10].selection, \n",
    "                                      mne_fixed_window_epochs['task/right'][0:10].selection])\n",
    "        \n",
    "        mne_fixed_window_epochs_finetune_test_samples = mne_fixed_window_epochs[new_test_samples]\n",
    "        labels_finetune_test_samples = mne_fixed_window_epochs_finetune_test_samples.events[:, -1]\n",
    "        \n",
    "        # Drop the 30 samples from the test set to avoid data leakage\n",
    "        mne_fixed_window_epochs_test = mne_fixed_window_epochs\n",
    "        with io.capture_output():\n",
    "            mne_fixed_window_epochs_test.drop(new_test_samples)\n",
    "        labels_test = mne_fixed_window_epochs_test.events[:, -1]\n",
    "        print(\"Loaded test epochs and extracted labels as well as new finetune test samples\")\n",
    "    else:  \n",
    "        epochs.append(mne_fixed_window_epochs)\n",
    "        labels_train.extend(mne_fixed_window_epochs.events[:, -1])\n",
    "        print(\"Loaded train epochs and extracted labels\")\n",
    "    \n",
    "# Make single epoch object for training\n",
    "with io.capture_output():\n",
    "    mne_fixed_window_epochs_train = mne.concatenate_epochs(epochs, verbose=False)\n",
    "print(\"\\n\\nConcatenated the train epochs\")\n",
    "print(f\"Total amount of train labels: {len(labels_train)}\")\n",
    "\n",
    "# Test epochs are of single trial already\n",
    "print(\"\\n\\nGot the test epochs\")\n",
    "print(f\"Total amount of test labels: {len(labels_test)}\")\n",
    "\n",
    "# Go to 2D representation\n",
    "labels_train = np.array(labels_train).reshape(-1, 1)\n",
    "labels_test = np.array(labels_test).reshape(-1, 1)\n",
    "labels_finetune_test_samples = np.array(labels_finetune_test_samples).reshape(-1, 1)\n",
    "\n",
    "# One Hot Encode the labels\n",
    "ohe = OneHotEncoder()\n",
    "ohe_labels_train = ohe.fit_transform(labels_train).toarray()\n",
    "ohe_labels_test = ohe.transform(labels_test).toarray()\n",
    "ohe_labels_finetune_test_samples = ohe.transform(labels_finetune_test_samples).toarray()\n",
    "\n",
    "# Validate OHE\n",
    "print(f\"\\nTrain labels OHE match regular labels: {np.array_equal(ohe.inverse_transform(ohe_labels_train), labels_train)}\")\n",
    "print(f\"Test labels OHE match regular labels: {np.array_equal(ohe.inverse_transform(ohe_labels_test), labels_test)}\")\n",
    "print(f\"Finetune labels OHE match regular labels: {np.array_equal(ohe.inverse_transform(ohe_labels_finetune_test_samples), labels_finetune_test_samples)}\")\n",
    "\n",
    "\n",
    "\n",
    "# Test epochs are of single trial already\n",
    "print(\"\\n\\nGot the finetune test samples epochs\")\n",
    "print(f\"Total amount of finetune labels: {len(labels_finetune_test_samples)}\")\n",
    "display(mne_fixed_window_epochs_finetune_test_samples)\n",
    "\n",
    "# Get training data\n",
    "with io.capture_output():\n",
    "    mne_fixed_window_epochs_train_data = mne_fixed_window_epochs_train.get_data(tmin=0.2, tmax=0.7)\n",
    "    mne_fixed_window_epochs_test_data = mne_fixed_window_epochs_test.get_data(tmin=0.2, tmax=0.7)\n",
    "    mne_fixed_window_epochs_finetune_test_samples_data = mne_fixed_window_epochs_finetune_test_samples.get_data(tmin=0.2, tmax=0.7)\n",
    "\n",
    "# Fix scaling sensitivity as MNE stores as data * 10e-6\n",
    "mne_fixed_window_epochs_train_data = mne_fixed_window_epochs_train_data * 1000000\n",
    "mne_fixed_window_epochs_test_data = mne_fixed_window_epochs_test_data * 1000000\n",
    "mne_fixed_window_epochs_finetune_test_samples_data = mne_fixed_window_epochs_finetune_test_samples_data * 1000000\n",
    "\n",
    "# Remove unused variables\n",
    "del new_test_samples\n",
    "del epochs\n",
    "del mne_fixed_window_epochs\n",
    "del test_trial\n",
    "del single_mne_raw\n",
    "del mne_raws\n",
    "del mne_fixed_window_epochs_train\n",
    "del mne_fixed_window_epochs_test\n",
    "del mne_fixed_window_epochs_finetune_test_samples\n",
    "del labels_train\n",
    "del labels_test\n",
    "del labels_finetune_test_samples\n",
    "del test_subject\n",
    "del test_idx"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2f70e7a",
   "metadata": {},
   "source": [
    "### Training the model on the train data\n",
    "\n",
    "We start by simply training the model on the available test data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "f4cef7b5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_2\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_3 (InputLayer)        [(None, 21, 100, 1)]      0         \n",
      "                                                                 \n",
      " conv2d_2 (Conv2D)           (None, 21, 100, 8)        400       \n",
      "                                                                 \n",
      " batch_normalization_6 (Batc  (None, 21, 100, 8)       32        \n",
      " hNormalization)                                                 \n",
      "                                                                 \n",
      " depthwise_conv2d_2 (Depthwi  (None, 1, 100, 16)       336       \n",
      " seConv2D)                                                       \n",
      "                                                                 \n",
      " batch_normalization_7 (Batc  (None, 1, 100, 16)       64        \n",
      " hNormalization)                                                 \n",
      "                                                                 \n",
      " activation_4 (Activation)   (None, 1, 100, 16)        0         \n",
      "                                                                 \n",
      " average_pooling2d_4 (Averag  (None, 1, 25, 16)        0         \n",
      " ePooling2D)                                                     \n",
      "                                                                 \n",
      " spatial_dropout2d_4 (Spatia  (None, 1, 25, 16)        0         \n",
      " lDropout2D)                                                     \n",
      "                                                                 \n",
      " separable_conv2d_2 (Separab  (None, 1, 25, 16)        512       \n",
      " leConv2D)                                                       \n",
      "                                                                 \n",
      " batch_normalization_8 (Batc  (None, 1, 25, 16)        64        \n",
      " hNormalization)                                                 \n",
      "                                                                 \n",
      " activation_5 (Activation)   (None, 1, 25, 16)         0         \n",
      "                                                                 \n",
      " average_pooling2d_5 (Averag  (None, 1, 3, 16)         0         \n",
      " ePooling2D)                                                     \n",
      "                                                                 \n",
      " spatial_dropout2d_5 (Spatia  (None, 1, 3, 16)         0         \n",
      " lDropout2D)                                                     \n",
      "                                                                 \n",
      " flatten (Flatten)           (None, 48)                0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 3)                 147       \n",
      "                                                                 \n",
      " softmax (Activation)        (None, 3)                 0         \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 1,555\n",
      "Trainable params: 1,475\n",
      "Non-trainable params: 80\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "####################################################\n",
    "# CREATE EEGNET MODEL\n",
    "####################################################\n",
    "\n",
    "# Create the TensorFlow Keras model\n",
    "keras_eegnet_model = EEGNet(\n",
    "    nb_classes = 3, # int, number of classes to classify. \n",
    "    Chans = 21, # number of channels in the EEG data. \n",
    "    Samples = 100, # number of time points in the EEG data. (default: 128)\n",
    "    dropoutRate = 0.5, # dropout fraction. (default: 0.5)\n",
    "    kernLength = 50, # length of temporal convolution in first layer. Suggested: half the sampling rate. (default: 64)\n",
    "    F1 = 8, # number of temporal filters. (default: 8)\n",
    "    F2 = 16, # number of pointwise filters. (default: 16)\n",
    "    D = 2, # number of spatial filters to learn within each temporal convolution. (default: 2)\n",
    "    norm_rate = 0.25, # Normalisation rate. (default: 0.25)\n",
    "    dropoutType = 'SpatialDropout2D' # Either SpatialDropout2D or Dropout, passed as a string. (default: Dropout)\n",
    "    )\n",
    "\n",
    "# Compile the model so it can be fitted\n",
    "# Loss and optimizer from EEGNet paper\n",
    "keras_eegnet_model.compile(loss= 'categorical_crossentropy', \n",
    "                           optimizer= tf.keras.optimizers.Adam(learning_rate= 0.001), \n",
    "                           metrics= [\"accuracy\"])\n",
    "\n",
    "# Show summary of the model\n",
    "keras_eegnet_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "0fecd45c",
   "metadata": {},
   "outputs": [],
   "source": [
    "####################################################\n",
    "# TRAIN BASE EEGNET MODEL\n",
    "####################################################\n",
    "\n",
    "# Configure\n",
    "retrain_model = False\n",
    "tensorboard_name = \"EEGNet_multisession_C0-C1-train_100hz\"\n",
    "best_base_model_filename = f\"./saved_variables/7/Base-EEGNet/{tensorboard_name}\"\n",
    "\n",
    "\n",
    "# Train on train/test split of data from one session\n",
    "## Note: the model is forced to use GPU, if GPU is not available replace with what is available e.g. /cpu:0\n",
    "## TODO: Change to CPU/GPU preference\n",
    "if (retrain_model): # Retrain or not\n",
    "    with tf.device('/gpu:0'):\n",
    "        keras_eegnet_model.fit(\n",
    "            x= mne_fixed_window_epochs_train_data,\n",
    "            y= ohe_labels_train,\n",
    "            batch_size= 128, # Default: 32\n",
    "            epochs= 500, # Default: 500 (EEGNet paper)\n",
    "            verbose= 1, # 0 = silent, 1 = progress bar, 2 = one line per epoch\n",
    "            callbacks= [TF_tools.tensorboard_callback(log_name= tensorboard_name),\n",
    "                        TF_tools.lowest_loss_model_save_callback(filepath= best_base_model_filename),\n",
    "                        TF_tools.highest_accuracy_model_save_callback(filepath= best_base_model_filename)],\n",
    "            validation_split= 0.3,\n",
    "            shuffle= True,\n",
    "            sample_weight= None, # Can be interesting due to time series\n",
    "            use_multiprocessing=True, # Done for faster speed\n",
    "            workers= 4 # Done for faster speed\n",
    "            )\n",
    "\n",
    "# Delete unused variables\n",
    "del retrain_model\n",
    "del tensorboard_name"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ee7d8ce",
   "metadata": {},
   "source": [
    "### Testing base model performance on unseen trial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "26f46aa1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results for lowest loss model on unseen trial without optimisations\n",
      "Accuracy of: 0.6167922497308934\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Results for highest accuracy model on unseen trial without optimisations\n",
      "Accuracy of: 0.6210979547900969\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "####################################################\n",
    "# BASE EEGNET MODEL PERFORMANCE\n",
    "####################################################\n",
    "\n",
    "# Convert labels back to original\n",
    "y_test = ohe.inverse_transform(ohe_labels_test)\n",
    "\n",
    "# Get results for best validation loss model\n",
    "print(\"Results for lowest loss model on unseen trial without optimisations\")\n",
    "keras_eegnet_model = TF_tools.load_lowest_loss_model(filepath= best_base_model_filename)\n",
    "\n",
    "y_pred = keras_eegnet_model.predict(mne_fixed_window_epochs_test_data)\n",
    "y_pred = ohe.inverse_transform(y_pred)\n",
    "\n",
    "accuracy =  accuracy_score(y_test, y_pred)\n",
    "\n",
    "print(f\"Accuracy of: {accuracy}\")\n",
    "ConfusionMatrixDisplay.from_predictions(y_true= y_test, y_pred= y_pred)\n",
    "plt.show()\n",
    "\n",
    "# Get results for best validation loss model\n",
    "print(\"\\n\\nResults for highest accuracy model on unseen trial without optimisations\")\n",
    "keras_eegnet_model = TF_tools.load_highest_accuracy_model(filepath= best_base_model_filename)\n",
    "\n",
    "y_pred = keras_eegnet_model.predict(mne_fixed_window_epochs_test_data)\n",
    "y_pred = ohe.inverse_transform(y_pred)\n",
    "\n",
    "accuracy =  accuracy_score(y_test, y_pred)\n",
    "\n",
    "print(f\"Accuracy of: {accuracy}\")\n",
    "ConfusionMatrixDisplay.from_predictions(y_true= y_test, y_pred= y_pred)\n",
    "plt.show()\n",
    "\n",
    "# Remove unused variables\n",
    "del accuracy\n",
    "del y_pred\n",
    "del y_test\n",
    "del keras_eegnet_model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "943fad70",
   "metadata": {},
   "source": [
    "### Do the fine-tuning\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "ec7d14ed",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 1.1991 - accuracy: 0.5625\n",
      "Epoch 1: val_loss improved from inf to 0.83803, saving model to ./saved_variables/7/finetune-EEGNet/EEGNet_multisession_C0-C1-train_C2-test-samples-finetune_100hz_lowest_loss_model.hdf5\n",
      "\n",
      "Epoch 1: val_accuracy improved from -inf to 0.66667, saving model to ./saved_variables/7/finetune-EEGNet/EEGNet_multisession_C0-C1-train_C2-test-samples-finetune_100hz_highest_acc_model.hdf5\n",
      "2/2 [==============================] - 1s 229ms/step - loss: 1.2415 - accuracy: 0.5417 - val_loss: 0.8380 - val_accuracy: 0.6667\n",
      "Epoch 2/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 1.0487 - accuracy: 0.5000\n",
      "Epoch 2: val_loss improved from 0.83803 to 0.83117, saving model to ./saved_variables/7/finetune-EEGNet/EEGNet_multisession_C0-C1-train_C2-test-samples-finetune_100hz_lowest_loss_model.hdf5\n",
      "\n",
      "Epoch 2: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 77ms/step - loss: 0.9411 - accuracy: 0.5417 - val_loss: 0.8312 - val_accuracy: 0.6667\n",
      "Epoch 3/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 1.5194 - accuracy: 0.5000\n",
      "Epoch 3: val_loss improved from 0.83117 to 0.82289, saving model to ./saved_variables/7/finetune-EEGNet/EEGNet_multisession_C0-C1-train_C2-test-samples-finetune_100hz_lowest_loss_model.hdf5\n",
      "\n",
      "Epoch 3: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 64ms/step - loss: 1.4317 - accuracy: 0.5833 - val_loss: 0.8229 - val_accuracy: 0.6667\n",
      "Epoch 4/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 1.3020 - accuracy: 0.5000\n",
      "Epoch 4: val_loss improved from 0.82289 to 0.81389, saving model to ./saved_variables/7/finetune-EEGNet/EEGNet_multisession_C0-C1-train_C2-test-samples-finetune_100hz_lowest_loss_model.hdf5\n",
      "\n",
      "Epoch 4: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 87ms/step - loss: 0.9623 - accuracy: 0.6250 - val_loss: 0.8139 - val_accuracy: 0.6667\n",
      "Epoch 5/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 1.0810 - accuracy: 0.6250\n",
      "Epoch 5: val_loss improved from 0.81389 to 0.80822, saving model to ./saved_variables/7/finetune-EEGNet/EEGNet_multisession_C0-C1-train_C2-test-samples-finetune_100hz_lowest_loss_model.hdf5\n",
      "\n",
      "Epoch 5: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 63ms/step - loss: 0.9208 - accuracy: 0.6667 - val_loss: 0.8082 - val_accuracy: 0.6667\n",
      "Epoch 6/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 1.1619 - accuracy: 0.6875\n",
      "Epoch 6: val_loss improved from 0.80822 to 0.79965, saving model to ./saved_variables/7/finetune-EEGNet/EEGNet_multisession_C0-C1-train_C2-test-samples-finetune_100hz_lowest_loss_model.hdf5\n",
      "\n",
      "Epoch 6: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 63ms/step - loss: 1.3136 - accuracy: 0.5833 - val_loss: 0.7997 - val_accuracy: 0.6667\n",
      "Epoch 7/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 1.3754 - accuracy: 0.5000\n",
      "Epoch 7: val_loss improved from 0.79965 to 0.78812, saving model to ./saved_variables/7/finetune-EEGNet/EEGNet_multisession_C0-C1-train_C2-test-samples-finetune_100hz_lowest_loss_model.hdf5\n",
      "\n",
      "Epoch 7: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 60ms/step - loss: 1.1186 - accuracy: 0.6250 - val_loss: 0.7881 - val_accuracy: 0.6667\n",
      "Epoch 8/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 1.7012 - accuracy: 0.4375\n",
      "Epoch 8: val_loss improved from 0.78812 to 0.78260, saving model to ./saved_variables/7/finetune-EEGNet/EEGNet_multisession_C0-C1-train_C2-test-samples-finetune_100hz_lowest_loss_model.hdf5\n",
      "\n",
      "Epoch 8: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 67ms/step - loss: 1.3701 - accuracy: 0.5417 - val_loss: 0.7826 - val_accuracy: 0.6667\n",
      "Epoch 9/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 1.6058 - accuracy: 0.5000\n",
      "Epoch 9: val_loss improved from 0.78260 to 0.77618, saving model to ./saved_variables/7/finetune-EEGNet/EEGNet_multisession_C0-C1-train_C2-test-samples-finetune_100hz_lowest_loss_model.hdf5\n",
      "\n",
      "Epoch 9: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 59ms/step - loss: 1.3655 - accuracy: 0.5417 - val_loss: 0.7762 - val_accuracy: 0.6667\n",
      "Epoch 10/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.8142 - accuracy: 0.7500\n",
      "Epoch 10: val_loss improved from 0.77618 to 0.76934, saving model to ./saved_variables/7/finetune-EEGNet/EEGNet_multisession_C0-C1-train_C2-test-samples-finetune_100hz_lowest_loss_model.hdf5\n",
      "\n",
      "Epoch 10: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 61ms/step - loss: 0.8212 - accuracy: 0.7083 - val_loss: 0.7693 - val_accuracy: 0.6667\n",
      "Epoch 11/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 1.0393 - accuracy: 0.6250\n",
      "Epoch 11: val_loss improved from 0.76934 to 0.76174, saving model to ./saved_variables/7/finetune-EEGNet/EEGNet_multisession_C0-C1-train_C2-test-samples-finetune_100hz_lowest_loss_model.hdf5\n",
      "\n",
      "Epoch 11: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 62ms/step - loss: 1.2860 - accuracy: 0.5833 - val_loss: 0.7617 - val_accuracy: 0.6667\n",
      "Epoch 12/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 1.6562 - accuracy: 0.5625\n",
      "Epoch 12: val_loss improved from 0.76174 to 0.74834, saving model to ./saved_variables/7/finetune-EEGNet/EEGNet_multisession_C0-C1-train_C2-test-samples-finetune_100hz_lowest_loss_model.hdf5\n",
      "\n",
      "Epoch 12: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 64ms/step - loss: 1.3847 - accuracy: 0.5417 - val_loss: 0.7483 - val_accuracy: 0.6667\n",
      "Epoch 13/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 1.3071 - accuracy: 0.4375\n",
      "Epoch 13: val_loss improved from 0.74834 to 0.74321, saving model to ./saved_variables/7/finetune-EEGNet/EEGNet_multisession_C0-C1-train_C2-test-samples-finetune_100hz_lowest_loss_model.hdf5\n",
      "\n",
      "Epoch 13: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 63ms/step - loss: 0.9608 - accuracy: 0.5833 - val_loss: 0.7432 - val_accuracy: 0.6667\n",
      "Epoch 14/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 1.0415 - accuracy: 0.6250\n",
      "Epoch 14: val_loss improved from 0.74321 to 0.73614, saving model to ./saved_variables/7/finetune-EEGNet/EEGNet_multisession_C0-C1-train_C2-test-samples-finetune_100hz_lowest_loss_model.hdf5\n",
      "\n",
      "Epoch 14: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 64ms/step - loss: 0.9965 - accuracy: 0.6667 - val_loss: 0.7361 - val_accuracy: 0.6667\n",
      "Epoch 15/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.8950 - accuracy: 0.6875\n",
      "Epoch 15: val_loss improved from 0.73614 to 0.72997, saving model to ./saved_variables/7/finetune-EEGNet/EEGNet_multisession_C0-C1-train_C2-test-samples-finetune_100hz_lowest_loss_model.hdf5\n",
      "\n",
      "Epoch 15: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 71ms/step - loss: 0.8304 - accuracy: 0.6667 - val_loss: 0.7300 - val_accuracy: 0.6667\n",
      "Epoch 16/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.9771 - accuracy: 0.6875\n",
      "Epoch 16: val_loss improved from 0.72997 to 0.72267, saving model to ./saved_variables/7/finetune-EEGNet/EEGNet_multisession_C0-C1-train_C2-test-samples-finetune_100hz_lowest_loss_model.hdf5\n",
      "\n",
      "Epoch 16: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 63ms/step - loss: 1.1419 - accuracy: 0.6667 - val_loss: 0.7227 - val_accuracy: 0.6667\n",
      "Epoch 17/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 1.3298 - accuracy: 0.5625\n",
      "Epoch 17: val_loss improved from 0.72267 to 0.71838, saving model to ./saved_variables/7/finetune-EEGNet/EEGNet_multisession_C0-C1-train_C2-test-samples-finetune_100hz_lowest_loss_model.hdf5\n",
      "\n",
      "Epoch 17: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 58ms/step - loss: 1.2231 - accuracy: 0.6250 - val_loss: 0.7184 - val_accuracy: 0.6667\n",
      "Epoch 18/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 1.0104 - accuracy: 0.6250\n",
      "Epoch 18: val_loss improved from 0.71838 to 0.71597, saving model to ./saved_variables/7/finetune-EEGNet/EEGNet_multisession_C0-C1-train_C2-test-samples-finetune_100hz_lowest_loss_model.hdf5\n",
      "\n",
      "Epoch 18: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 57ms/step - loss: 0.9388 - accuracy: 0.6667 - val_loss: 0.7160 - val_accuracy: 0.6667\n",
      "Epoch 19/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.7532 - accuracy: 0.5625\n",
      "Epoch 19: val_loss improved from 0.71597 to 0.71181, saving model to ./saved_variables/7/finetune-EEGNet/EEGNet_multisession_C0-C1-train_C2-test-samples-finetune_100hz_lowest_loss_model.hdf5\n",
      "\n",
      "Epoch 19: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 59ms/step - loss: 0.9929 - accuracy: 0.5833 - val_loss: 0.7118 - val_accuracy: 0.6667\n",
      "Epoch 20/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.4404 - accuracy: 0.9375\n",
      "Epoch 20: val_loss improved from 0.71181 to 0.70726, saving model to ./saved_variables/7/finetune-EEGNet/EEGNet_multisession_C0-C1-train_C2-test-samples-finetune_100hz_lowest_loss_model.hdf5\n",
      "\n",
      "Epoch 20: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 66ms/step - loss: 0.7902 - accuracy: 0.7917 - val_loss: 0.7073 - val_accuracy: 0.6667\n",
      "Epoch 21/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.9564 - accuracy: 0.6250\n",
      "Epoch 21: val_loss improved from 0.70726 to 0.70377, saving model to ./saved_variables/7/finetune-EEGNet/EEGNet_multisession_C0-C1-train_C2-test-samples-finetune_100hz_lowest_loss_model.hdf5\n",
      "\n",
      "Epoch 21: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 67ms/step - loss: 0.8732 - accuracy: 0.6667 - val_loss: 0.7038 - val_accuracy: 0.6667\n",
      "Epoch 22/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 1.0610 - accuracy: 0.5625\n",
      "Epoch 22: val_loss improved from 0.70377 to 0.69695, saving model to ./saved_variables/7/finetune-EEGNet/EEGNet_multisession_C0-C1-train_C2-test-samples-finetune_100hz_lowest_loss_model.hdf5\n",
      "\n",
      "Epoch 22: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 70ms/step - loss: 1.0540 - accuracy: 0.5417 - val_loss: 0.6970 - val_accuracy: 0.6667\n",
      "Epoch 23/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.7318 - accuracy: 0.6875\n",
      "Epoch 23: val_loss improved from 0.69695 to 0.69159, saving model to ./saved_variables/7/finetune-EEGNet/EEGNet_multisession_C0-C1-train_C2-test-samples-finetune_100hz_lowest_loss_model.hdf5\n",
      "\n",
      "Epoch 23: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 64ms/step - loss: 0.7997 - accuracy: 0.6250 - val_loss: 0.6916 - val_accuracy: 0.6667\n",
      "Epoch 24/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.7351 - accuracy: 0.6250\n",
      "Epoch 24: val_loss improved from 0.69159 to 0.68708, saving model to ./saved_variables/7/finetune-EEGNet/EEGNet_multisession_C0-C1-train_C2-test-samples-finetune_100hz_lowest_loss_model.hdf5\n",
      "\n",
      "Epoch 24: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 70ms/step - loss: 0.9010 - accuracy: 0.6667 - val_loss: 0.6871 - val_accuracy: 0.6667\n",
      "Epoch 25/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.8848 - accuracy: 0.7500\n",
      "Epoch 25: val_loss improved from 0.68708 to 0.68216, saving model to ./saved_variables/7/finetune-EEGNet/EEGNet_multisession_C0-C1-train_C2-test-samples-finetune_100hz_lowest_loss_model.hdf5\n",
      "\n",
      "Epoch 25: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 69ms/step - loss: 0.9262 - accuracy: 0.7083 - val_loss: 0.6822 - val_accuracy: 0.6667\n",
      "Epoch 26/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 1.2344 - accuracy: 0.6875\n",
      "Epoch 26: val_loss improved from 0.68216 to 0.67869, saving model to ./saved_variables/7/finetune-EEGNet/EEGNet_multisession_C0-C1-train_C2-test-samples-finetune_100hz_lowest_loss_model.hdf5\n",
      "\n",
      "Epoch 26: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 57ms/step - loss: 0.9346 - accuracy: 0.7500 - val_loss: 0.6787 - val_accuracy: 0.6667\n",
      "Epoch 27/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 1.0268 - accuracy: 0.6250\n",
      "Epoch 27: val_loss improved from 0.67869 to 0.67237, saving model to ./saved_variables/7/finetune-EEGNet/EEGNet_multisession_C0-C1-train_C2-test-samples-finetune_100hz_lowest_loss_model.hdf5\n",
      "\n",
      "Epoch 27: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 79ms/step - loss: 1.0889 - accuracy: 0.5833 - val_loss: 0.6724 - val_accuracy: 0.6667\n",
      "Epoch 28/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.9378 - accuracy: 0.6250\n",
      "Epoch 28: val_loss improved from 0.67237 to 0.66624, saving model to ./saved_variables/7/finetune-EEGNet/EEGNet_multisession_C0-C1-train_C2-test-samples-finetune_100hz_lowest_loss_model.hdf5\n",
      "\n",
      "Epoch 28: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 83ms/step - loss: 0.9453 - accuracy: 0.6250 - val_loss: 0.6662 - val_accuracy: 0.6667\n",
      "Epoch 29/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.9638 - accuracy: 0.6250\n",
      "Epoch 29: val_loss improved from 0.66624 to 0.66260, saving model to ./saved_variables/7/finetune-EEGNet/EEGNet_multisession_C0-C1-train_C2-test-samples-finetune_100hz_lowest_loss_model.hdf5\n",
      "\n",
      "Epoch 29: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 81ms/step - loss: 0.8944 - accuracy: 0.6667 - val_loss: 0.6626 - val_accuracy: 0.6667\n",
      "Epoch 30/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.9111 - accuracy: 0.4375\n",
      "Epoch 30: val_loss improved from 0.66260 to 0.65791, saving model to ./saved_variables/7/finetune-EEGNet/EEGNet_multisession_C0-C1-train_C2-test-samples-finetune_100hz_lowest_loss_model.hdf5\n",
      "\n",
      "Epoch 30: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 78ms/step - loss: 0.8659 - accuracy: 0.5000 - val_loss: 0.6579 - val_accuracy: 0.6667\n",
      "Epoch 31/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 1.2373 - accuracy: 0.6250\n",
      "Epoch 31: val_loss improved from 0.65791 to 0.65430, saving model to ./saved_variables/7/finetune-EEGNet/EEGNet_multisession_C0-C1-train_C2-test-samples-finetune_100hz_lowest_loss_model.hdf5\n",
      "\n",
      "Epoch 31: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 63ms/step - loss: 0.9644 - accuracy: 0.7083 - val_loss: 0.6543 - val_accuracy: 0.6667\n",
      "Epoch 32/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 1.2662 - accuracy: 0.5000\n",
      "Epoch 32: val_loss improved from 0.65430 to 0.64673, saving model to ./saved_variables/7/finetune-EEGNet/EEGNet_multisession_C0-C1-train_C2-test-samples-finetune_100hz_lowest_loss_model.hdf5\n",
      "\n",
      "Epoch 32: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 57ms/step - loss: 0.9304 - accuracy: 0.6667 - val_loss: 0.6467 - val_accuracy: 0.6667\n",
      "Epoch 33/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 1.2641 - accuracy: 0.4375\n",
      "Epoch 33: val_loss improved from 0.64673 to 0.64178, saving model to ./saved_variables/7/finetune-EEGNet/EEGNet_multisession_C0-C1-train_C2-test-samples-finetune_100hz_lowest_loss_model.hdf5\n",
      "\n",
      "Epoch 33: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 60ms/step - loss: 1.0381 - accuracy: 0.5000 - val_loss: 0.6418 - val_accuracy: 0.6667\n",
      "Epoch 34/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.7554 - accuracy: 0.6875\n",
      "Epoch 34: val_loss improved from 0.64178 to 0.63728, saving model to ./saved_variables/7/finetune-EEGNet/EEGNet_multisession_C0-C1-train_C2-test-samples-finetune_100hz_lowest_loss_model.hdf5\n",
      "\n",
      "Epoch 34: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 62ms/step - loss: 0.6720 - accuracy: 0.7500 - val_loss: 0.6373 - val_accuracy: 0.6667\n",
      "Epoch 35/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.8766 - accuracy: 0.5625\n",
      "Epoch 35: val_loss improved from 0.63728 to 0.63047, saving model to ./saved_variables/7/finetune-EEGNet/EEGNet_multisession_C0-C1-train_C2-test-samples-finetune_100hz_lowest_loss_model.hdf5\n",
      "\n",
      "Epoch 35: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 60ms/step - loss: 0.9183 - accuracy: 0.5417 - val_loss: 0.6305 - val_accuracy: 0.6667\n",
      "Epoch 36/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.6412 - accuracy: 0.7500\n",
      "Epoch 36: val_loss improved from 0.63047 to 0.62469, saving model to ./saved_variables/7/finetune-EEGNet/EEGNet_multisession_C0-C1-train_C2-test-samples-finetune_100hz_lowest_loss_model.hdf5\n",
      "\n",
      "Epoch 36: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 61ms/step - loss: 0.5699 - accuracy: 0.7083 - val_loss: 0.6247 - val_accuracy: 0.6667\n",
      "Epoch 37/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.6169 - accuracy: 0.8125\n",
      "Epoch 37: val_loss improved from 0.62469 to 0.61900, saving model to ./saved_variables/7/finetune-EEGNet/EEGNet_multisession_C0-C1-train_C2-test-samples-finetune_100hz_lowest_loss_model.hdf5\n",
      "\n",
      "Epoch 37: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 65ms/step - loss: 0.7250 - accuracy: 0.7917 - val_loss: 0.6190 - val_accuracy: 0.6667\n",
      "Epoch 38/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.7663 - accuracy: 0.6250\n",
      "Epoch 38: val_loss improved from 0.61900 to 0.61616, saving model to ./saved_variables/7/finetune-EEGNet/EEGNet_multisession_C0-C1-train_C2-test-samples-finetune_100hz_lowest_loss_model.hdf5\n",
      "\n",
      "Epoch 38: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 69ms/step - loss: 0.8786 - accuracy: 0.6667 - val_loss: 0.6162 - val_accuracy: 0.6667\n",
      "Epoch 39/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 1.0277 - accuracy: 0.6250\n",
      "Epoch 39: val_loss improved from 0.61616 to 0.61120, saving model to ./saved_variables/7/finetune-EEGNet/EEGNet_multisession_C0-C1-train_C2-test-samples-finetune_100hz_lowest_loss_model.hdf5\n",
      "\n",
      "Epoch 39: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 68ms/step - loss: 0.9241 - accuracy: 0.6250 - val_loss: 0.6112 - val_accuracy: 0.6667\n",
      "Epoch 40/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.8404 - accuracy: 0.7500\n",
      "Epoch 40: val_loss improved from 0.61120 to 0.60899, saving model to ./saved_variables/7/finetune-EEGNet/EEGNet_multisession_C0-C1-train_C2-test-samples-finetune_100hz_lowest_loss_model.hdf5\n",
      "\n",
      "Epoch 40: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 70ms/step - loss: 0.8467 - accuracy: 0.7500 - val_loss: 0.6090 - val_accuracy: 0.6667\n",
      "Epoch 41/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 1.1286 - accuracy: 0.6250\n",
      "Epoch 41: val_loss improved from 0.60899 to 0.60483, saving model to ./saved_variables/7/finetune-EEGNet/EEGNet_multisession_C0-C1-train_C2-test-samples-finetune_100hz_lowest_loss_model.hdf5\n",
      "\n",
      "Epoch 41: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 61ms/step - loss: 0.9953 - accuracy: 0.6250 - val_loss: 0.6048 - val_accuracy: 0.6667\n",
      "Epoch 42/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.7031 - accuracy: 0.7500\n",
      "Epoch 42: val_loss improved from 0.60483 to 0.59980, saving model to ./saved_variables/7/finetune-EEGNet/EEGNet_multisession_C0-C1-train_C2-test-samples-finetune_100hz_lowest_loss_model.hdf5\n",
      "\n",
      "Epoch 42: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 57ms/step - loss: 0.9479 - accuracy: 0.6667 - val_loss: 0.5998 - val_accuracy: 0.6667\n",
      "Epoch 43/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.7749 - accuracy: 0.6250\n",
      "Epoch 43: val_loss improved from 0.59980 to 0.59400, saving model to ./saved_variables/7/finetune-EEGNet/EEGNet_multisession_C0-C1-train_C2-test-samples-finetune_100hz_lowest_loss_model.hdf5\n",
      "\n",
      "Epoch 43: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 59ms/step - loss: 0.8503 - accuracy: 0.6250 - val_loss: 0.5940 - val_accuracy: 0.6667\n",
      "Epoch 44/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 1.1233 - accuracy: 0.7500\n",
      "Epoch 44: val_loss improved from 0.59400 to 0.59191, saving model to ./saved_variables/7/finetune-EEGNet/EEGNet_multisession_C0-C1-train_C2-test-samples-finetune_100hz_lowest_loss_model.hdf5\n",
      "\n",
      "Epoch 44: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 59ms/step - loss: 0.8782 - accuracy: 0.7917 - val_loss: 0.5919 - val_accuracy: 0.6667\n",
      "Epoch 45/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.8565 - accuracy: 0.6875\n",
      "Epoch 45: val_loss improved from 0.59191 to 0.58874, saving model to ./saved_variables/7/finetune-EEGNet/EEGNet_multisession_C0-C1-train_C2-test-samples-finetune_100hz_lowest_loss_model.hdf5\n",
      "\n",
      "Epoch 45: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 59ms/step - loss: 0.7190 - accuracy: 0.7083 - val_loss: 0.5887 - val_accuracy: 0.6667\n",
      "Epoch 46/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.4308 - accuracy: 0.8750\n",
      "Epoch 46: val_loss improved from 0.58874 to 0.58696, saving model to ./saved_variables/7/finetune-EEGNet/EEGNet_multisession_C0-C1-train_C2-test-samples-finetune_100hz_lowest_loss_model.hdf5\n",
      "\n",
      "Epoch 46: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 58ms/step - loss: 0.5384 - accuracy: 0.7917 - val_loss: 0.5870 - val_accuracy: 0.6667\n",
      "Epoch 47/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.5916 - accuracy: 0.7500\n",
      "Epoch 47: val_loss improved from 0.58696 to 0.58364, saving model to ./saved_variables/7/finetune-EEGNet/EEGNet_multisession_C0-C1-train_C2-test-samples-finetune_100hz_lowest_loss_model.hdf5\n",
      "\n",
      "Epoch 47: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 60ms/step - loss: 0.6056 - accuracy: 0.7083 - val_loss: 0.5836 - val_accuracy: 0.6667\n",
      "Epoch 48/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.5663 - accuracy: 0.7500\n",
      "Epoch 48: val_loss improved from 0.58364 to 0.57884, saving model to ./saved_variables/7/finetune-EEGNet/EEGNet_multisession_C0-C1-train_C2-test-samples-finetune_100hz_lowest_loss_model.hdf5\n",
      "\n",
      "Epoch 48: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 63ms/step - loss: 0.7705 - accuracy: 0.6250 - val_loss: 0.5788 - val_accuracy: 0.6667\n",
      "Epoch 49/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.7733 - accuracy: 0.5625\n",
      "Epoch 49: val_loss improved from 0.57884 to 0.57719, saving model to ./saved_variables/7/finetune-EEGNet/EEGNet_multisession_C0-C1-train_C2-test-samples-finetune_100hz_lowest_loss_model.hdf5\n",
      "\n",
      "Epoch 49: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 59ms/step - loss: 0.7785 - accuracy: 0.5833 - val_loss: 0.5772 - val_accuracy: 0.6667\n",
      "Epoch 50/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.7651 - accuracy: 0.7500\n",
      "Epoch 50: val_loss improved from 0.57719 to 0.57440, saving model to ./saved_variables/7/finetune-EEGNet/EEGNet_multisession_C0-C1-train_C2-test-samples-finetune_100hz_lowest_loss_model.hdf5\n",
      "\n",
      "Epoch 50: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 59ms/step - loss: 0.8344 - accuracy: 0.7083 - val_loss: 0.5744 - val_accuracy: 0.6667\n",
      "Epoch 51/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.7424 - accuracy: 0.6250\n",
      "Epoch 51: val_loss improved from 0.57440 to 0.57042, saving model to ./saved_variables/7/finetune-EEGNet/EEGNet_multisession_C0-C1-train_C2-test-samples-finetune_100hz_lowest_loss_model.hdf5\n",
      "\n",
      "Epoch 51: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 64ms/step - loss: 0.6896 - accuracy: 0.6667 - val_loss: 0.5704 - val_accuracy: 0.6667\n",
      "Epoch 52/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 1.1453 - accuracy: 0.6875\n",
      "Epoch 52: val_loss improved from 0.57042 to 0.56874, saving model to ./saved_variables/7/finetune-EEGNet/EEGNet_multisession_C0-C1-train_C2-test-samples-finetune_100hz_lowest_loss_model.hdf5\n",
      "\n",
      "Epoch 52: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 59ms/step - loss: 0.9445 - accuracy: 0.6667 - val_loss: 0.5687 - val_accuracy: 0.6667\n",
      "Epoch 53/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.6642 - accuracy: 0.6875\n",
      "Epoch 53: val_loss improved from 0.56874 to 0.56371, saving model to ./saved_variables/7/finetune-EEGNet/EEGNet_multisession_C0-C1-train_C2-test-samples-finetune_100hz_lowest_loss_model.hdf5\n",
      "\n",
      "Epoch 53: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 57ms/step - loss: 0.7315 - accuracy: 0.6667 - val_loss: 0.5637 - val_accuracy: 0.6667\n",
      "Epoch 54/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.4995 - accuracy: 0.8125\n",
      "Epoch 54: val_loss improved from 0.56371 to 0.56035, saving model to ./saved_variables/7/finetune-EEGNet/EEGNet_multisession_C0-C1-train_C2-test-samples-finetune_100hz_lowest_loss_model.hdf5\n",
      "\n",
      "Epoch 54: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 58ms/step - loss: 0.8575 - accuracy: 0.7083 - val_loss: 0.5603 - val_accuracy: 0.6667\n",
      "Epoch 55/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.9369 - accuracy: 0.6250\n",
      "Epoch 55: val_loss improved from 0.56035 to 0.55439, saving model to ./saved_variables/7/finetune-EEGNet/EEGNet_multisession_C0-C1-train_C2-test-samples-finetune_100hz_lowest_loss_model.hdf5\n",
      "\n",
      "Epoch 55: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 61ms/step - loss: 0.8888 - accuracy: 0.6250 - val_loss: 0.5544 - val_accuracy: 0.6667\n",
      "Epoch 56/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.7098 - accuracy: 0.7500\n",
      "Epoch 56: val_loss improved from 0.55439 to 0.54585, saving model to ./saved_variables/7/finetune-EEGNet/EEGNet_multisession_C0-C1-train_C2-test-samples-finetune_100hz_lowest_loss_model.hdf5\n",
      "\n",
      "Epoch 56: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 59ms/step - loss: 0.8634 - accuracy: 0.6667 - val_loss: 0.5459 - val_accuracy: 0.6667\n",
      "Epoch 57/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.7808 - accuracy: 0.7500\n",
      "Epoch 57: val_loss improved from 0.54585 to 0.54297, saving model to ./saved_variables/7/finetune-EEGNet/EEGNet_multisession_C0-C1-train_C2-test-samples-finetune_100hz_lowest_loss_model.hdf5\n",
      "\n",
      "Epoch 57: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 59ms/step - loss: 0.6671 - accuracy: 0.7917 - val_loss: 0.5430 - val_accuracy: 0.6667\n",
      "Epoch 58/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.8643 - accuracy: 0.6875\n",
      "Epoch 58: val_loss improved from 0.54297 to 0.54014, saving model to ./saved_variables/7/finetune-EEGNet/EEGNet_multisession_C0-C1-train_C2-test-samples-finetune_100hz_lowest_loss_model.hdf5\n",
      "\n",
      "Epoch 58: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 66ms/step - loss: 0.6587 - accuracy: 0.7917 - val_loss: 0.5401 - val_accuracy: 0.6667\n",
      "Epoch 59/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.7668 - accuracy: 0.7500\n",
      "Epoch 59: val_loss improved from 0.54014 to 0.53726, saving model to ./saved_variables/7/finetune-EEGNet/EEGNet_multisession_C0-C1-train_C2-test-samples-finetune_100hz_lowest_loss_model.hdf5\n",
      "\n",
      "Epoch 59: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 65ms/step - loss: 0.8974 - accuracy: 0.7500 - val_loss: 0.5373 - val_accuracy: 0.6667\n",
      "Epoch 60/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.6203 - accuracy: 0.6250\n",
      "Epoch 60: val_loss improved from 0.53726 to 0.53432, saving model to ./saved_variables/7/finetune-EEGNet/EEGNet_multisession_C0-C1-train_C2-test-samples-finetune_100hz_lowest_loss_model.hdf5\n",
      "\n",
      "Epoch 60: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 75ms/step - loss: 0.5654 - accuracy: 0.6667 - val_loss: 0.5343 - val_accuracy: 0.6667\n",
      "Epoch 61/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.5457 - accuracy: 0.8750\n",
      "Epoch 61: val_loss improved from 0.53432 to 0.53050, saving model to ./saved_variables/7/finetune-EEGNet/EEGNet_multisession_C0-C1-train_C2-test-samples-finetune_100hz_lowest_loss_model.hdf5\n",
      "\n",
      "Epoch 61: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 70ms/step - loss: 0.7222 - accuracy: 0.7083 - val_loss: 0.5305 - val_accuracy: 0.6667\n",
      "Epoch 62/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.9009 - accuracy: 0.6875\n",
      "Epoch 62: val_loss improved from 0.53050 to 0.52864, saving model to ./saved_variables/7/finetune-EEGNet/EEGNet_multisession_C0-C1-train_C2-test-samples-finetune_100hz_lowest_loss_model.hdf5\n",
      "\n",
      "Epoch 62: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 72ms/step - loss: 0.6788 - accuracy: 0.7917 - val_loss: 0.5286 - val_accuracy: 0.6667\n",
      "Epoch 63/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.6340 - accuracy: 0.6875\n",
      "Epoch 63: val_loss improved from 0.52864 to 0.52626, saving model to ./saved_variables/7/finetune-EEGNet/EEGNet_multisession_C0-C1-train_C2-test-samples-finetune_100hz_lowest_loss_model.hdf5\n",
      "\n",
      "Epoch 63: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 65ms/step - loss: 0.6952 - accuracy: 0.7083 - val_loss: 0.5263 - val_accuracy: 0.6667\n",
      "Epoch 64/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.5481 - accuracy: 0.8125\n",
      "Epoch 64: val_loss did not improve from 0.52626\n",
      "\n",
      "Epoch 64: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 30ms/step - loss: 0.7370 - accuracy: 0.7083 - val_loss: 0.5268 - val_accuracy: 0.6667\n",
      "Epoch 65/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 1.0010 - accuracy: 0.5625\n",
      "Epoch 65: val_loss did not improve from 0.52626\n",
      "\n",
      "Epoch 65: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 33ms/step - loss: 0.9754 - accuracy: 0.5833 - val_loss: 0.5265 - val_accuracy: 0.6667\n",
      "Epoch 66/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.5784 - accuracy: 0.7500\n",
      "Epoch 66: val_loss did not improve from 0.52626\n",
      "\n",
      "Epoch 66: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 50ms/step - loss: 0.7469 - accuracy: 0.7500 - val_loss: 0.5265 - val_accuracy: 0.6667\n",
      "Epoch 67/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.6309 - accuracy: 0.6250\n",
      "Epoch 67: val_loss did not improve from 0.52626\n",
      "\n",
      "Epoch 67: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 31ms/step - loss: 0.7023 - accuracy: 0.7083 - val_loss: 0.5265 - val_accuracy: 0.6667\n",
      "Epoch 68/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.4513 - accuracy: 0.8125\n",
      "Epoch 68: val_loss did not improve from 0.52626\n",
      "\n",
      "Epoch 68: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 29ms/step - loss: 0.4686 - accuracy: 0.7917 - val_loss: 0.5270 - val_accuracy: 0.6667\n",
      "Epoch 69/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.7334 - accuracy: 0.7500\n",
      "Epoch 69: val_loss improved from 0.52626 to 0.52609, saving model to ./saved_variables/7/finetune-EEGNet/EEGNet_multisession_C0-C1-train_C2-test-samples-finetune_100hz_lowest_loss_model.hdf5\n",
      "\n",
      "Epoch 69: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 84ms/step - loss: 0.6378 - accuracy: 0.7917 - val_loss: 0.5261 - val_accuracy: 0.6667\n",
      "Epoch 70/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.6750 - accuracy: 0.6875\n",
      "Epoch 70: val_loss improved from 0.52609 to 0.52297, saving model to ./saved_variables/7/finetune-EEGNet/EEGNet_multisession_C0-C1-train_C2-test-samples-finetune_100hz_lowest_loss_model.hdf5\n",
      "\n",
      "Epoch 70: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 60ms/step - loss: 0.6107 - accuracy: 0.7083 - val_loss: 0.5230 - val_accuracy: 0.6667\n",
      "Epoch 71/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.8204 - accuracy: 0.6875\n",
      "Epoch 71: val_loss improved from 0.52297 to 0.51897, saving model to ./saved_variables/7/finetune-EEGNet/EEGNet_multisession_C0-C1-train_C2-test-samples-finetune_100hz_lowest_loss_model.hdf5\n",
      "\n",
      "Epoch 71: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 57ms/step - loss: 0.8198 - accuracy: 0.6667 - val_loss: 0.5190 - val_accuracy: 0.6667\n",
      "Epoch 72/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.7554 - accuracy: 0.5625\n",
      "Epoch 72: val_loss improved from 0.51897 to 0.51891, saving model to ./saved_variables/7/finetune-EEGNet/EEGNet_multisession_C0-C1-train_C2-test-samples-finetune_100hz_lowest_loss_model.hdf5\n",
      "\n",
      "Epoch 72: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 60ms/step - loss: 0.6375 - accuracy: 0.6667 - val_loss: 0.5189 - val_accuracy: 0.6667\n",
      "Epoch 73/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.7433 - accuracy: 0.6250\n",
      "Epoch 73: val_loss improved from 0.51891 to 0.51653, saving model to ./saved_variables/7/finetune-EEGNet/EEGNet_multisession_C0-C1-train_C2-test-samples-finetune_100hz_lowest_loss_model.hdf5\n",
      "\n",
      "Epoch 73: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 73ms/step - loss: 0.6001 - accuracy: 0.7083 - val_loss: 0.5165 - val_accuracy: 0.6667\n",
      "Epoch 74/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.6575 - accuracy: 0.7500\n",
      "Epoch 74: val_loss did not improve from 0.51653\n",
      "\n",
      "Epoch 74: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 41ms/step - loss: 0.7217 - accuracy: 0.7500 - val_loss: 0.5169 - val_accuracy: 0.6667\n",
      "Epoch 75/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.5613 - accuracy: 0.7500\n",
      "Epoch 75: val_loss improved from 0.51653 to 0.51536, saving model to ./saved_variables/7/finetune-EEGNet/EEGNet_multisession_C0-C1-train_C2-test-samples-finetune_100hz_lowest_loss_model.hdf5\n",
      "\n",
      "Epoch 75: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 108ms/step - loss: 0.5795 - accuracy: 0.7083 - val_loss: 0.5154 - val_accuracy: 0.6667\n",
      "Epoch 76/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.7551 - accuracy: 0.6875\n",
      "Epoch 76: val_loss did not improve from 0.51536\n",
      "\n",
      "Epoch 76: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 53ms/step - loss: 0.6673 - accuracy: 0.7083 - val_loss: 0.5158 - val_accuracy: 0.6667\n",
      "Epoch 77/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.7993 - accuracy: 0.6875\n",
      "Epoch 77: val_loss improved from 0.51536 to 0.51132, saving model to ./saved_variables/7/finetune-EEGNet/EEGNet_multisession_C0-C1-train_C2-test-samples-finetune_100hz_lowest_loss_model.hdf5\n",
      "\n",
      "Epoch 77: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 127ms/step - loss: 0.8226 - accuracy: 0.6667 - val_loss: 0.5113 - val_accuracy: 0.6667\n",
      "Epoch 78/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.7800 - accuracy: 0.5625\n",
      "Epoch 78: val_loss improved from 0.51132 to 0.51089, saving model to ./saved_variables/7/finetune-EEGNet/EEGNet_multisession_C0-C1-train_C2-test-samples-finetune_100hz_lowest_loss_model.hdf5\n",
      "\n",
      "Epoch 78: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 78ms/step - loss: 0.6274 - accuracy: 0.7083 - val_loss: 0.5109 - val_accuracy: 0.6667\n",
      "Epoch 79/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 1.0983 - accuracy: 0.5000\n",
      "Epoch 79: val_loss improved from 0.51089 to 0.51018, saving model to ./saved_variables/7/finetune-EEGNet/EEGNet_multisession_C0-C1-train_C2-test-samples-finetune_100hz_lowest_loss_model.hdf5\n",
      "\n",
      "Epoch 79: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 64ms/step - loss: 0.8888 - accuracy: 0.5833 - val_loss: 0.5102 - val_accuracy: 0.6667\n",
      "Epoch 80/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.8081 - accuracy: 0.8125\n",
      "Epoch 80: val_loss improved from 0.51018 to 0.50781, saving model to ./saved_variables/7/finetune-EEGNet/EEGNet_multisession_C0-C1-train_C2-test-samples-finetune_100hz_lowest_loss_model.hdf5\n",
      "\n",
      "Epoch 80: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 59ms/step - loss: 0.9094 - accuracy: 0.7083 - val_loss: 0.5078 - val_accuracy: 0.6667\n",
      "Epoch 81/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.5263 - accuracy: 0.8750\n",
      "Epoch 81: val_loss improved from 0.50781 to 0.50628, saving model to ./saved_variables/7/finetune-EEGNet/EEGNet_multisession_C0-C1-train_C2-test-samples-finetune_100hz_lowest_loss_model.hdf5\n",
      "\n",
      "Epoch 81: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 57ms/step - loss: 0.6087 - accuracy: 0.8333 - val_loss: 0.5063 - val_accuracy: 0.6667\n",
      "Epoch 82/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.5836 - accuracy: 0.7500\n",
      "Epoch 82: val_loss did not improve from 0.50628\n",
      "\n",
      "Epoch 82: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 33ms/step - loss: 0.5908 - accuracy: 0.7083 - val_loss: 0.5084 - val_accuracy: 0.6667\n",
      "Epoch 83/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.6708 - accuracy: 0.7500\n",
      "Epoch 83: val_loss did not improve from 0.50628\n",
      "\n",
      "Epoch 83: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 39ms/step - loss: 0.6125 - accuracy: 0.7500 - val_loss: 0.5094 - val_accuracy: 0.6667\n",
      "Epoch 84/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.5092 - accuracy: 0.7500\n",
      "Epoch 84: val_loss did not improve from 0.50628\n",
      "\n",
      "Epoch 84: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 33ms/step - loss: 0.5439 - accuracy: 0.7500 - val_loss: 0.5100 - val_accuracy: 0.6667\n",
      "Epoch 85/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.7390 - accuracy: 0.6875\n",
      "Epoch 85: val_loss did not improve from 0.50628\n",
      "\n",
      "Epoch 85: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 33ms/step - loss: 0.5483 - accuracy: 0.7917 - val_loss: 0.5101 - val_accuracy: 0.6667\n",
      "Epoch 86/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.3537 - accuracy: 0.8125\n",
      "Epoch 86: val_loss did not improve from 0.50628\n",
      "\n",
      "Epoch 86: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 32ms/step - loss: 0.4762 - accuracy: 0.8333 - val_loss: 0.5109 - val_accuracy: 0.6667\n",
      "Epoch 87/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.6138 - accuracy: 0.7500\n",
      "Epoch 87: val_loss did not improve from 0.50628\n",
      "\n",
      "Epoch 87: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 28ms/step - loss: 0.6747 - accuracy: 0.6667 - val_loss: 0.5090 - val_accuracy: 0.6667\n",
      "Epoch 88/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 1.0101 - accuracy: 0.6875\n",
      "Epoch 88: val_loss did not improve from 0.50628\n",
      "\n",
      "Epoch 88: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 28ms/step - loss: 0.7572 - accuracy: 0.7500 - val_loss: 0.5096 - val_accuracy: 0.6667\n",
      "Epoch 89/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.4462 - accuracy: 0.8125\n",
      "Epoch 89: val_loss improved from 0.50628 to 0.50537, saving model to ./saved_variables/7/finetune-EEGNet/EEGNet_multisession_C0-C1-train_C2-test-samples-finetune_100hz_lowest_loss_model.hdf5\n",
      "\n",
      "Epoch 89: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 53ms/step - loss: 0.4741 - accuracy: 0.8333 - val_loss: 0.5054 - val_accuracy: 0.6667\n",
      "Epoch 90/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.8279 - accuracy: 0.5625\n",
      "Epoch 90: val_loss improved from 0.50537 to 0.50171, saving model to ./saved_variables/7/finetune-EEGNet/EEGNet_multisession_C0-C1-train_C2-test-samples-finetune_100hz_lowest_loss_model.hdf5\n",
      "\n",
      "Epoch 90: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 54ms/step - loss: 0.7641 - accuracy: 0.6250 - val_loss: 0.5017 - val_accuracy: 0.6667\n",
      "Epoch 91/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.6759 - accuracy: 0.7500\n",
      "Epoch 91: val_loss improved from 0.50171 to 0.50071, saving model to ./saved_variables/7/finetune-EEGNet/EEGNet_multisession_C0-C1-train_C2-test-samples-finetune_100hz_lowest_loss_model.hdf5\n",
      "\n",
      "Epoch 91: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 53ms/step - loss: 0.5537 - accuracy: 0.7917 - val_loss: 0.5007 - val_accuracy: 0.6667\n",
      "Epoch 92/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.5571 - accuracy: 0.7500\n",
      "Epoch 92: val_loss improved from 0.50071 to 0.50055, saving model to ./saved_variables/7/finetune-EEGNet/EEGNet_multisession_C0-C1-train_C2-test-samples-finetune_100hz_lowest_loss_model.hdf5\n",
      "\n",
      "Epoch 92: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 56ms/step - loss: 0.5790 - accuracy: 0.7500 - val_loss: 0.5006 - val_accuracy: 0.6667\n",
      "Epoch 93/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.7808 - accuracy: 0.6250\n",
      "Epoch 93: val_loss improved from 0.50055 to 0.49978, saving model to ./saved_variables/7/finetune-EEGNet/EEGNet_multisession_C0-C1-train_C2-test-samples-finetune_100hz_lowest_loss_model.hdf5\n",
      "\n",
      "Epoch 93: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 51ms/step - loss: 0.7986 - accuracy: 0.6667 - val_loss: 0.4998 - val_accuracy: 0.6667\n",
      "Epoch 94/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.5685 - accuracy: 0.6875\n",
      "Epoch 94: val_loss did not improve from 0.49978\n",
      "\n",
      "Epoch 94: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 28ms/step - loss: 0.5855 - accuracy: 0.6667 - val_loss: 0.4998 - val_accuracy: 0.6667\n",
      "Epoch 95/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.4369 - accuracy: 0.7500\n",
      "Epoch 95: val_loss improved from 0.49978 to 0.49892, saving model to ./saved_variables/7/finetune-EEGNet/EEGNet_multisession_C0-C1-train_C2-test-samples-finetune_100hz_lowest_loss_model.hdf5\n",
      "\n",
      "Epoch 95: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 51ms/step - loss: 0.5811 - accuracy: 0.7500 - val_loss: 0.4989 - val_accuracy: 0.6667\n",
      "Epoch 96/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.2936 - accuracy: 1.0000\n",
      "Epoch 96: val_loss improved from 0.49892 to 0.49610, saving model to ./saved_variables/7/finetune-EEGNet/EEGNet_multisession_C0-C1-train_C2-test-samples-finetune_100hz_lowest_loss_model.hdf5\n",
      "\n",
      "Epoch 96: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 56ms/step - loss: 0.5188 - accuracy: 0.9167 - val_loss: 0.4961 - val_accuracy: 0.6667\n",
      "Epoch 97/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.6797 - accuracy: 0.6875\n",
      "Epoch 97: val_loss did not improve from 0.49610\n",
      "\n",
      "Epoch 97: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 28ms/step - loss: 0.6570 - accuracy: 0.7083 - val_loss: 0.4974 - val_accuracy: 0.6667\n",
      "Epoch 98/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.2959 - accuracy: 0.9375\n",
      "Epoch 98: val_loss did not improve from 0.49610\n",
      "\n",
      "Epoch 98: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 35ms/step - loss: 0.5644 - accuracy: 0.7917 - val_loss: 0.4984 - val_accuracy: 0.6667\n",
      "Epoch 99/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.4114 - accuracy: 0.8125\n",
      "Epoch 99: val_loss did not improve from 0.49610\n",
      "\n",
      "Epoch 99: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 31ms/step - loss: 0.4494 - accuracy: 0.7917 - val_loss: 0.4979 - val_accuracy: 0.6667\n",
      "Epoch 100/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.6135 - accuracy: 0.6875\n",
      "Epoch 100: val_loss did not improve from 0.49610\n",
      "\n",
      "Epoch 100: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 32ms/step - loss: 0.6627 - accuracy: 0.7083 - val_loss: 0.4985 - val_accuracy: 0.6667\n",
      "Epoch 101/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.4532 - accuracy: 0.8125\n",
      "Epoch 101: val_loss did not improve from 0.49610\n",
      "\n",
      "Epoch 101: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 30ms/step - loss: 0.4155 - accuracy: 0.7917 - val_loss: 0.4971 - val_accuracy: 0.6667\n",
      "Epoch 102/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.5761 - accuracy: 0.8125\n",
      "Epoch 102: val_loss did not improve from 0.49610\n",
      "\n",
      "Epoch 102: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 31ms/step - loss: 0.5123 - accuracy: 0.8750 - val_loss: 0.4980 - val_accuracy: 0.6667\n",
      "Epoch 103/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.6551 - accuracy: 0.8125\n",
      "Epoch 103: val_loss improved from 0.49610 to 0.49443, saving model to ./saved_variables/7/finetune-EEGNet/EEGNet_multisession_C0-C1-train_C2-test-samples-finetune_100hz_lowest_loss_model.hdf5\n",
      "\n",
      "Epoch 103: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 63ms/step - loss: 0.6023 - accuracy: 0.8333 - val_loss: 0.4944 - val_accuracy: 0.6667\n",
      "Epoch 104/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.3492 - accuracy: 0.9375\n",
      "Epoch 104: val_loss improved from 0.49443 to 0.49206, saving model to ./saved_variables/7/finetune-EEGNet/EEGNet_multisession_C0-C1-train_C2-test-samples-finetune_100hz_lowest_loss_model.hdf5\n",
      "\n",
      "Epoch 104: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 104ms/step - loss: 0.4497 - accuracy: 0.8333 - val_loss: 0.4921 - val_accuracy: 0.6667\n",
      "Epoch 105/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.4137 - accuracy: 0.7500\n",
      "Epoch 105: val_loss did not improve from 0.49206\n",
      "\n",
      "Epoch 105: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 50ms/step - loss: 0.4858 - accuracy: 0.7917 - val_loss: 0.4925 - val_accuracy: 0.6667\n",
      "Epoch 106/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.4719 - accuracy: 0.8125\n",
      "Epoch 106: val_loss did not improve from 0.49206\n",
      "\n",
      "Epoch 106: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 48ms/step - loss: 0.3783 - accuracy: 0.8750 - val_loss: 0.4943 - val_accuracy: 0.6667\n",
      "Epoch 107/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.6975 - accuracy: 0.7500\n",
      "Epoch 107: val_loss did not improve from 0.49206\n",
      "\n",
      "Epoch 107: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 52ms/step - loss: 0.5367 - accuracy: 0.8333 - val_loss: 0.4953 - val_accuracy: 0.6667\n",
      "Epoch 108/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.5201 - accuracy: 0.8125\n",
      "Epoch 108: val_loss did not improve from 0.49206\n",
      "\n",
      "Epoch 108: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 45ms/step - loss: 0.5200 - accuracy: 0.7500 - val_loss: 0.4945 - val_accuracy: 0.6667\n",
      "Epoch 109/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.4157 - accuracy: 0.8125\n",
      "Epoch 109: val_loss did not improve from 0.49206\n",
      "\n",
      "Epoch 109: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 52ms/step - loss: 0.5025 - accuracy: 0.8333 - val_loss: 0.4928 - val_accuracy: 0.6667\n",
      "Epoch 110/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.5750 - accuracy: 0.6875\n",
      "Epoch 110: val_loss did not improve from 0.49206\n",
      "\n",
      "Epoch 110: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 49ms/step - loss: 0.6547 - accuracy: 0.7083 - val_loss: 0.4939 - val_accuracy: 0.6667\n",
      "Epoch 111/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.5572 - accuracy: 0.7500\n",
      "Epoch 111: val_loss did not improve from 0.49206\n",
      "\n",
      "Epoch 111: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 31ms/step - loss: 0.6554 - accuracy: 0.7500 - val_loss: 0.4948 - val_accuracy: 0.6667\n",
      "Epoch 112/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.7861 - accuracy: 0.6250\n",
      "Epoch 112: val_loss did not improve from 0.49206\n",
      "\n",
      "Epoch 112: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 32ms/step - loss: 0.6862 - accuracy: 0.6667 - val_loss: 0.4970 - val_accuracy: 0.6667\n",
      "Epoch 113/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.5696 - accuracy: 0.7500\n",
      "Epoch 113: val_loss did not improve from 0.49206\n",
      "\n",
      "Epoch 113: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 33ms/step - loss: 0.5569 - accuracy: 0.7917 - val_loss: 0.4935 - val_accuracy: 0.6667\n",
      "Epoch 114/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.4966 - accuracy: 0.8750\n",
      "Epoch 114: val_loss did not improve from 0.49206\n",
      "\n",
      "Epoch 114: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 34ms/step - loss: 0.5127 - accuracy: 0.8333 - val_loss: 0.4934 - val_accuracy: 0.6667\n",
      "Epoch 115/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.4404 - accuracy: 0.9375\n",
      "Epoch 115: val_loss did not improve from 0.49206\n",
      "\n",
      "Epoch 115: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 45ms/step - loss: 0.6457 - accuracy: 0.8333 - val_loss: 0.4923 - val_accuracy: 0.6667\n",
      "Epoch 116/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.7732 - accuracy: 0.6875\n",
      "Epoch 116: val_loss improved from 0.49206 to 0.49146, saving model to ./saved_variables/7/finetune-EEGNet/EEGNet_multisession_C0-C1-train_C2-test-samples-finetune_100hz_lowest_loss_model.hdf5\n",
      "\n",
      "Epoch 116: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 100ms/step - loss: 0.6564 - accuracy: 0.7500 - val_loss: 0.4915 - val_accuracy: 0.6667\n",
      "Epoch 117/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.4616 - accuracy: 0.8125\n",
      "Epoch 117: val_loss improved from 0.49146 to 0.48982, saving model to ./saved_variables/7/finetune-EEGNet/EEGNet_multisession_C0-C1-train_C2-test-samples-finetune_100hz_lowest_loss_model.hdf5\n",
      "\n",
      "Epoch 117: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 72ms/step - loss: 0.4405 - accuracy: 0.8333 - val_loss: 0.4898 - val_accuracy: 0.6667\n",
      "Epoch 118/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.6620 - accuracy: 0.6875\n",
      "Epoch 118: val_loss did not improve from 0.48982\n",
      "\n",
      "Epoch 118: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 35ms/step - loss: 0.6289 - accuracy: 0.7083 - val_loss: 0.4905 - val_accuracy: 0.6667\n",
      "Epoch 119/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.7801 - accuracy: 0.6875\n",
      "Epoch 119: val_loss did not improve from 0.48982\n",
      "\n",
      "Epoch 119: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 58ms/step - loss: 0.5778 - accuracy: 0.7917 - val_loss: 0.4922 - val_accuracy: 0.6667\n",
      "Epoch 120/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.3300 - accuracy: 0.8125\n",
      "Epoch 120: val_loss did not improve from 0.48982\n",
      "\n",
      "Epoch 120: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 31ms/step - loss: 0.3217 - accuracy: 0.8750 - val_loss: 0.4939 - val_accuracy: 0.6667\n",
      "Epoch 121/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.8759 - accuracy: 0.6250\n",
      "Epoch 121: val_loss did not improve from 0.48982\n",
      "\n",
      "Epoch 121: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 32ms/step - loss: 0.6759 - accuracy: 0.7083 - val_loss: 0.4947 - val_accuracy: 0.6667\n",
      "Epoch 122/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.2994 - accuracy: 0.9375\n",
      "Epoch 122: val_loss did not improve from 0.48982\n",
      "\n",
      "Epoch 122: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 32ms/step - loss: 0.4169 - accuracy: 0.8750 - val_loss: 0.4955 - val_accuracy: 0.6667\n",
      "Epoch 123/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.4479 - accuracy: 0.9375\n",
      "Epoch 123: val_loss did not improve from 0.48982\n",
      "\n",
      "Epoch 123: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 35ms/step - loss: 0.6168 - accuracy: 0.8750 - val_loss: 0.4932 - val_accuracy: 0.6667\n",
      "Epoch 124/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.5581 - accuracy: 0.6875\n",
      "Epoch 124: val_loss improved from 0.48982 to 0.48965, saving model to ./saved_variables/7/finetune-EEGNet/EEGNet_multisession_C0-C1-train_C2-test-samples-finetune_100hz_lowest_loss_model.hdf5\n",
      "\n",
      "Epoch 124: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 69ms/step - loss: 0.5085 - accuracy: 0.7500 - val_loss: 0.4896 - val_accuracy: 0.6667\n",
      "Epoch 125/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.4340 - accuracy: 0.8125\n",
      "Epoch 125: val_loss improved from 0.48965 to 0.48664, saving model to ./saved_variables/7/finetune-EEGNet/EEGNet_multisession_C0-C1-train_C2-test-samples-finetune_100hz_lowest_loss_model.hdf5\n",
      "\n",
      "Epoch 125: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 67ms/step - loss: 0.5637 - accuracy: 0.7917 - val_loss: 0.4866 - val_accuracy: 0.6667\n",
      "Epoch 126/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.6292 - accuracy: 0.7500\n",
      "Epoch 126: val_loss did not improve from 0.48664\n",
      "\n",
      "Epoch 126: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 40ms/step - loss: 0.4563 - accuracy: 0.8333 - val_loss: 0.4871 - val_accuracy: 0.6667\n",
      "Epoch 127/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.4719 - accuracy: 0.7500\n",
      "Epoch 127: val_loss improved from 0.48664 to 0.48653, saving model to ./saved_variables/7/finetune-EEGNet/EEGNet_multisession_C0-C1-train_C2-test-samples-finetune_100hz_lowest_loss_model.hdf5\n",
      "\n",
      "Epoch 127: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 75ms/step - loss: 0.5232 - accuracy: 0.7500 - val_loss: 0.4865 - val_accuracy: 0.6667\n",
      "Epoch 128/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.4813 - accuracy: 0.8125\n",
      "Epoch 128: val_loss improved from 0.48653 to 0.48488, saving model to ./saved_variables/7/finetune-EEGNet/EEGNet_multisession_C0-C1-train_C2-test-samples-finetune_100hz_lowest_loss_model.hdf5\n",
      "\n",
      "Epoch 128: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 101ms/step - loss: 0.4855 - accuracy: 0.7917 - val_loss: 0.4849 - val_accuracy: 0.6667\n",
      "Epoch 129/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.5283 - accuracy: 0.7500\n",
      "Epoch 129: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 129: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 34ms/step - loss: 0.5726 - accuracy: 0.7083 - val_loss: 0.4855 - val_accuracy: 0.6667\n",
      "Epoch 130/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.5827 - accuracy: 0.8125\n",
      "Epoch 130: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 130: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 31ms/step - loss: 0.5391 - accuracy: 0.8333 - val_loss: 0.4879 - val_accuracy: 0.6667\n",
      "Epoch 131/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.6457 - accuracy: 0.8125\n",
      "Epoch 131: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 131: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 39ms/step - loss: 0.4997 - accuracy: 0.8750 - val_loss: 0.4884 - val_accuracy: 0.6667\n",
      "Epoch 132/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.4568 - accuracy: 0.8125\n",
      "Epoch 132: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 132: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 33ms/step - loss: 0.5246 - accuracy: 0.8333 - val_loss: 0.4882 - val_accuracy: 0.6667\n",
      "Epoch 133/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.3453 - accuracy: 0.8750\n",
      "Epoch 133: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 133: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 33ms/step - loss: 0.4803 - accuracy: 0.7917 - val_loss: 0.4896 - val_accuracy: 0.6667\n",
      "Epoch 134/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.5546 - accuracy: 0.7500\n",
      "Epoch 134: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 134: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 31ms/step - loss: 0.4819 - accuracy: 0.7917 - val_loss: 0.4900 - val_accuracy: 0.6667\n",
      "Epoch 135/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.4864 - accuracy: 0.8125\n",
      "Epoch 135: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 135: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 38ms/step - loss: 0.5888 - accuracy: 0.7500 - val_loss: 0.4914 - val_accuracy: 0.6667\n",
      "Epoch 136/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.3880 - accuracy: 0.8750\n",
      "Epoch 136: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 136: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 32ms/step - loss: 0.4727 - accuracy: 0.7917 - val_loss: 0.4897 - val_accuracy: 0.6667\n",
      "Epoch 137/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.4262 - accuracy: 0.8750\n",
      "Epoch 137: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 137: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 31ms/step - loss: 0.4175 - accuracy: 0.8750 - val_loss: 0.4906 - val_accuracy: 0.6667\n",
      "Epoch 138/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.4264 - accuracy: 0.7500\n",
      "Epoch 138: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 138: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 36ms/step - loss: 0.3862 - accuracy: 0.7917 - val_loss: 0.4879 - val_accuracy: 0.6667\n",
      "Epoch 139/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.4764 - accuracy: 0.8125\n",
      "Epoch 139: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 139: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 31ms/step - loss: 0.5411 - accuracy: 0.7917 - val_loss: 0.4862 - val_accuracy: 0.6667\n",
      "Epoch 140/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.3909 - accuracy: 0.8750\n",
      "Epoch 140: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 140: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 31ms/step - loss: 0.4022 - accuracy: 0.8750 - val_loss: 0.4885 - val_accuracy: 0.6667\n",
      "Epoch 141/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.3743 - accuracy: 0.8750\n",
      "Epoch 141: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 141: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 37ms/step - loss: 0.3388 - accuracy: 0.9167 - val_loss: 0.4881 - val_accuracy: 0.6667\n",
      "Epoch 142/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.5260 - accuracy: 0.8125\n",
      "Epoch 142: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 142: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 56ms/step - loss: 0.4568 - accuracy: 0.8333 - val_loss: 0.4885 - val_accuracy: 0.6667\n",
      "Epoch 143/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.5507 - accuracy: 0.8750\n",
      "Epoch 143: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 143: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 31ms/step - loss: 0.6322 - accuracy: 0.7917 - val_loss: 0.4885 - val_accuracy: 0.6667\n",
      "Epoch 144/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.4639 - accuracy: 0.8125\n",
      "Epoch 144: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 144: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 31ms/step - loss: 0.4693 - accuracy: 0.8333 - val_loss: 0.4915 - val_accuracy: 0.6667\n",
      "Epoch 145/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.3957 - accuracy: 0.8750\n",
      "Epoch 145: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 145: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 33ms/step - loss: 0.3794 - accuracy: 0.8750 - val_loss: 0.4923 - val_accuracy: 0.6667\n",
      "Epoch 146/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.3832 - accuracy: 0.8125\n",
      "Epoch 146: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 146: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 30ms/step - loss: 0.6163 - accuracy: 0.7083 - val_loss: 0.4884 - val_accuracy: 0.6667\n",
      "Epoch 147/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.3109 - accuracy: 0.8750\n",
      "Epoch 147: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 147: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 30ms/step - loss: 0.3070 - accuracy: 0.8750 - val_loss: 0.4882 - val_accuracy: 0.6667\n",
      "Epoch 148/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.3597 - accuracy: 0.8750\n",
      "Epoch 148: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 148: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 31ms/step - loss: 0.3549 - accuracy: 0.8750 - val_loss: 0.4914 - val_accuracy: 0.6667\n",
      "Epoch 149/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.4618 - accuracy: 0.7500\n",
      "Epoch 149: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 149: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 32ms/step - loss: 0.3878 - accuracy: 0.7917 - val_loss: 0.4882 - val_accuracy: 0.6667\n",
      "Epoch 150/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.3625 - accuracy: 0.8750\n",
      "Epoch 150: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 150: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 32ms/step - loss: 0.3645 - accuracy: 0.8750 - val_loss: 0.4891 - val_accuracy: 0.6667\n",
      "Epoch 151/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.4196 - accuracy: 0.8125\n",
      "Epoch 151: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 151: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 37ms/step - loss: 0.4972 - accuracy: 0.8333 - val_loss: 0.4906 - val_accuracy: 0.6667\n",
      "Epoch 152/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.4235 - accuracy: 0.8125\n",
      "Epoch 152: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 152: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 35ms/step - loss: 0.3853 - accuracy: 0.8750 - val_loss: 0.4912 - val_accuracy: 0.6667\n",
      "Epoch 153/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.5109 - accuracy: 0.8125\n",
      "Epoch 153: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 153: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 30ms/step - loss: 0.5317 - accuracy: 0.7917 - val_loss: 0.4908 - val_accuracy: 0.6667\n",
      "Epoch 154/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.3963 - accuracy: 0.8750\n",
      "Epoch 154: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 154: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 35ms/step - loss: 0.4872 - accuracy: 0.8750 - val_loss: 0.4921 - val_accuracy: 0.6667\n",
      "Epoch 155/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.4496 - accuracy: 0.7500\n",
      "Epoch 155: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 155: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 32ms/step - loss: 0.3695 - accuracy: 0.7917 - val_loss: 0.4953 - val_accuracy: 0.6667\n",
      "Epoch 156/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.1934 - accuracy: 1.0000\n",
      "Epoch 156: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 156: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 30ms/step - loss: 0.2666 - accuracy: 0.9583 - val_loss: 0.4972 - val_accuracy: 0.6667\n",
      "Epoch 157/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.5267 - accuracy: 0.8125\n",
      "Epoch 157: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 157: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 61ms/step - loss: 0.4232 - accuracy: 0.8333 - val_loss: 0.4995 - val_accuracy: 0.6667\n",
      "Epoch 158/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.6508 - accuracy: 0.7500\n",
      "Epoch 158: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 158: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 31ms/step - loss: 0.7010 - accuracy: 0.6667 - val_loss: 0.5012 - val_accuracy: 0.6667\n",
      "Epoch 159/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.3969 - accuracy: 0.7500\n",
      "Epoch 159: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 159: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 32ms/step - loss: 0.4084 - accuracy: 0.7917 - val_loss: 0.5007 - val_accuracy: 0.6667\n",
      "Epoch 160/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.5728 - accuracy: 0.8125\n",
      "Epoch 160: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 160: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 37ms/step - loss: 0.5078 - accuracy: 0.7917 - val_loss: 0.5057 - val_accuracy: 0.6667\n",
      "Epoch 161/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.3288 - accuracy: 0.9375\n",
      "Epoch 161: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 161: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 31ms/step - loss: 0.4798 - accuracy: 0.8750 - val_loss: 0.5088 - val_accuracy: 0.6667\n",
      "Epoch 162/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.5080 - accuracy: 0.6875\n",
      "Epoch 162: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 162: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 30ms/step - loss: 0.5278 - accuracy: 0.7083 - val_loss: 0.5111 - val_accuracy: 0.6667\n",
      "Epoch 163/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.4602 - accuracy: 0.8750\n",
      "Epoch 163: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 163: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 35ms/step - loss: 0.3940 - accuracy: 0.9167 - val_loss: 0.5125 - val_accuracy: 0.6667\n",
      "Epoch 164/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.3580 - accuracy: 0.8750\n",
      "Epoch 164: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 164: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 30ms/step - loss: 0.3585 - accuracy: 0.8333 - val_loss: 0.5134 - val_accuracy: 0.6667\n",
      "Epoch 165/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.4228 - accuracy: 0.8125\n",
      "Epoch 165: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 165: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 33ms/step - loss: 0.4407 - accuracy: 0.8333 - val_loss: 0.5157 - val_accuracy: 0.6667\n",
      "Epoch 166/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.6140 - accuracy: 0.6875\n",
      "Epoch 166: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 166: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 32ms/step - loss: 0.4898 - accuracy: 0.7917 - val_loss: 0.5199 - val_accuracy: 0.6667\n",
      "Epoch 167/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.3479 - accuracy: 0.9375\n",
      "Epoch 167: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 167: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 31ms/step - loss: 0.5046 - accuracy: 0.9167 - val_loss: 0.5204 - val_accuracy: 0.6667\n",
      "Epoch 168/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.5291 - accuracy: 0.8125\n",
      "Epoch 168: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 168: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 36ms/step - loss: 0.5084 - accuracy: 0.7500 - val_loss: 0.5237 - val_accuracy: 0.6667\n",
      "Epoch 169/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.5378 - accuracy: 0.8125\n",
      "Epoch 169: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 169: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 48ms/step - loss: 0.4959 - accuracy: 0.8333 - val_loss: 0.5279 - val_accuracy: 0.6667\n",
      "Epoch 170/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.3002 - accuracy: 1.0000\n",
      "Epoch 170: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 170: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 49ms/step - loss: 0.3131 - accuracy: 0.9583 - val_loss: 0.5329 - val_accuracy: 0.6667\n",
      "Epoch 171/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.3800 - accuracy: 0.9375\n",
      "Epoch 171: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 171: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 44ms/step - loss: 0.5934 - accuracy: 0.8333 - val_loss: 0.5366 - val_accuracy: 0.6667\n",
      "Epoch 172/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.4880 - accuracy: 0.8750\n",
      "Epoch 172: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 172: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 53ms/step - loss: 0.5710 - accuracy: 0.8333 - val_loss: 0.5381 - val_accuracy: 0.6667\n",
      "Epoch 173/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.6230 - accuracy: 0.7500\n",
      "Epoch 173: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 173: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 64ms/step - loss: 0.5682 - accuracy: 0.7500 - val_loss: 0.5391 - val_accuracy: 0.6667\n",
      "Epoch 174/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.5032 - accuracy: 0.8125\n",
      "Epoch 174: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 174: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 38ms/step - loss: 0.5384 - accuracy: 0.7917 - val_loss: 0.5407 - val_accuracy: 0.6667\n",
      "Epoch 175/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.4290 - accuracy: 0.8125\n",
      "Epoch 175: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 175: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 36ms/step - loss: 0.6576 - accuracy: 0.7917 - val_loss: 0.5439 - val_accuracy: 0.6667\n",
      "Epoch 176/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.2953 - accuracy: 0.9375\n",
      "Epoch 176: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 176: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 38ms/step - loss: 0.3542 - accuracy: 0.8750 - val_loss: 0.5452 - val_accuracy: 0.6667\n",
      "Epoch 177/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.3211 - accuracy: 0.9375\n",
      "Epoch 177: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 177: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 32ms/step - loss: 0.3289 - accuracy: 0.8750 - val_loss: 0.5463 - val_accuracy: 0.6667\n",
      "Epoch 178/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.3541 - accuracy: 0.8125\n",
      "Epoch 178: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 178: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 33ms/step - loss: 0.4747 - accuracy: 0.7500 - val_loss: 0.5472 - val_accuracy: 0.6667\n",
      "Epoch 179/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.5253 - accuracy: 0.6875\n",
      "Epoch 179: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 179: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 29ms/step - loss: 0.4621 - accuracy: 0.7500 - val_loss: 0.5489 - val_accuracy: 0.6667\n",
      "Epoch 180/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.3610 - accuracy: 0.9375\n",
      "Epoch 180: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 180: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 29ms/step - loss: 0.4074 - accuracy: 0.8750 - val_loss: 0.5486 - val_accuracy: 0.6667\n",
      "Epoch 181/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.5626 - accuracy: 0.6875\n",
      "Epoch 181: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 181: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 33ms/step - loss: 0.5412 - accuracy: 0.7083 - val_loss: 0.5520 - val_accuracy: 0.6667\n",
      "Epoch 182/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.4316 - accuracy: 0.8750\n",
      "Epoch 182: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 182: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 44ms/step - loss: 0.4221 - accuracy: 0.8750 - val_loss: 0.5567 - val_accuracy: 0.6667\n",
      "Epoch 183/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.3392 - accuracy: 0.8125\n",
      "Epoch 183: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 183: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 29ms/step - loss: 0.3987 - accuracy: 0.7917 - val_loss: 0.5591 - val_accuracy: 0.6667\n",
      "Epoch 184/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.6855 - accuracy: 0.7500\n",
      "Epoch 184: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 184: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 30ms/step - loss: 0.6554 - accuracy: 0.7917 - val_loss: 0.5589 - val_accuracy: 0.6667\n",
      "Epoch 185/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.5003 - accuracy: 0.7500\n",
      "Epoch 185: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 185: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 32ms/step - loss: 0.3837 - accuracy: 0.8333 - val_loss: 0.5623 - val_accuracy: 0.6667\n",
      "Epoch 186/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.5191 - accuracy: 0.8125\n",
      "Epoch 186: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 186: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 29ms/step - loss: 0.4945 - accuracy: 0.8333 - val_loss: 0.5633 - val_accuracy: 0.6667\n",
      "Epoch 187/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.6331 - accuracy: 0.7500\n",
      "Epoch 187: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 187: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 40ms/step - loss: 0.5269 - accuracy: 0.7917 - val_loss: 0.5662 - val_accuracy: 0.6667\n",
      "Epoch 188/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.7710 - accuracy: 0.6250\n",
      "Epoch 188: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 188: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 31ms/step - loss: 0.6068 - accuracy: 0.7500 - val_loss: 0.5677 - val_accuracy: 0.6667\n",
      "Epoch 189/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.4302 - accuracy: 0.8125\n",
      "Epoch 189: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 189: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 34ms/step - loss: 0.3872 - accuracy: 0.8333 - val_loss: 0.5681 - val_accuracy: 0.6667\n",
      "Epoch 190/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.4986 - accuracy: 0.9375\n",
      "Epoch 190: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 190: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 30ms/step - loss: 0.6151 - accuracy: 0.8750 - val_loss: 0.5685 - val_accuracy: 0.6667\n",
      "Epoch 191/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.3477 - accuracy: 0.9375\n",
      "Epoch 191: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 191: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 29ms/step - loss: 0.3655 - accuracy: 0.8750 - val_loss: 0.5716 - val_accuracy: 0.6667\n",
      "Epoch 192/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.3920 - accuracy: 0.8125\n",
      "Epoch 192: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 192: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 40ms/step - loss: 0.4085 - accuracy: 0.7917 - val_loss: 0.5735 - val_accuracy: 0.6667\n",
      "Epoch 193/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.5538 - accuracy: 0.8750\n",
      "Epoch 193: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 193: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 32ms/step - loss: 0.4594 - accuracy: 0.8750 - val_loss: 0.5742 - val_accuracy: 0.6667\n",
      "Epoch 194/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.5330 - accuracy: 0.6250\n",
      "Epoch 194: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 194: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 46ms/step - loss: 0.4650 - accuracy: 0.7083 - val_loss: 0.5736 - val_accuracy: 0.6667\n",
      "Epoch 195/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.2983 - accuracy: 0.8125\n",
      "Epoch 195: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 195: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 30ms/step - loss: 0.2941 - accuracy: 0.8750 - val_loss: 0.5768 - val_accuracy: 0.6667\n",
      "Epoch 196/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.3951 - accuracy: 0.8750\n",
      "Epoch 196: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 196: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 65ms/step - loss: 0.3265 - accuracy: 0.9167 - val_loss: 0.5770 - val_accuracy: 0.6667\n",
      "Epoch 197/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.5236 - accuracy: 0.8125\n",
      "Epoch 197: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 197: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 32ms/step - loss: 0.4793 - accuracy: 0.8333 - val_loss: 0.5764 - val_accuracy: 0.6667\n",
      "Epoch 198/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.5189 - accuracy: 0.8750\n",
      "Epoch 198: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 198: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 33ms/step - loss: 0.4836 - accuracy: 0.8750 - val_loss: 0.5781 - val_accuracy: 0.6667\n",
      "Epoch 199/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.2023 - accuracy: 0.9375\n",
      "Epoch 199: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 199: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 30ms/step - loss: 0.2447 - accuracy: 0.9583 - val_loss: 0.5805 - val_accuracy: 0.6667\n",
      "Epoch 200/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.6295 - accuracy: 0.7500\n",
      "Epoch 200: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 200: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 34ms/step - loss: 0.4972 - accuracy: 0.7917 - val_loss: 0.5866 - val_accuracy: 0.6667\n",
      "Epoch 201/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.4451 - accuracy: 0.8125\n",
      "Epoch 201: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 201: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 36ms/step - loss: 0.4670 - accuracy: 0.7917 - val_loss: 0.5871 - val_accuracy: 0.6667\n",
      "Epoch 202/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.3449 - accuracy: 0.8750\n",
      "Epoch 202: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 202: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 68ms/step - loss: 0.5041 - accuracy: 0.8333 - val_loss: 0.5898 - val_accuracy: 0.6667\n",
      "Epoch 203/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.4382 - accuracy: 0.8125\n",
      "Epoch 203: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 203: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 36ms/step - loss: 0.4170 - accuracy: 0.8333 - val_loss: 0.5961 - val_accuracy: 0.6667\n",
      "Epoch 204/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.2111 - accuracy: 0.9375\n",
      "Epoch 204: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 204: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 33ms/step - loss: 0.2645 - accuracy: 0.9167 - val_loss: 0.5946 - val_accuracy: 0.6667\n",
      "Epoch 205/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.2500 - accuracy: 0.9375\n",
      "Epoch 205: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 205: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 37ms/step - loss: 0.3929 - accuracy: 0.9167 - val_loss: 0.5949 - val_accuracy: 0.6667\n",
      "Epoch 206/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.3876 - accuracy: 0.9375\n",
      "Epoch 206: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 206: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 39ms/step - loss: 0.4732 - accuracy: 0.8750 - val_loss: 0.5989 - val_accuracy: 0.6667\n",
      "Epoch 207/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.3793 - accuracy: 0.8750\n",
      "Epoch 207: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 207: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 32ms/step - loss: 0.3771 - accuracy: 0.8750 - val_loss: 0.6035 - val_accuracy: 0.6667\n",
      "Epoch 208/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.4689 - accuracy: 0.8750\n",
      "Epoch 208: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 208: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 43ms/step - loss: 0.3821 - accuracy: 0.9167 - val_loss: 0.6036 - val_accuracy: 0.6667\n",
      "Epoch 209/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.3995 - accuracy: 0.9375\n",
      "Epoch 209: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 209: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 49ms/step - loss: 0.4652 - accuracy: 0.8333 - val_loss: 0.6040 - val_accuracy: 0.6667\n",
      "Epoch 210/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.2572 - accuracy: 0.9375\n",
      "Epoch 210: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 210: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 42ms/step - loss: 0.4020 - accuracy: 0.8750 - val_loss: 0.6078 - val_accuracy: 0.6667\n",
      "Epoch 211/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.4206 - accuracy: 0.8125\n",
      "Epoch 211: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 211: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 37ms/step - loss: 0.3818 - accuracy: 0.8333 - val_loss: 0.6103 - val_accuracy: 0.6667\n",
      "Epoch 212/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.3137 - accuracy: 0.9375\n",
      "Epoch 212: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 212: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 32ms/step - loss: 0.3378 - accuracy: 0.9167 - val_loss: 0.6094 - val_accuracy: 0.6667\n",
      "Epoch 213/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.4750 - accuracy: 0.8125\n",
      "Epoch 213: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 213: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 31ms/step - loss: 0.3765 - accuracy: 0.8750 - val_loss: 0.6082 - val_accuracy: 0.6667\n",
      "Epoch 214/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.4536 - accuracy: 0.8750\n",
      "Epoch 214: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 214: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 0.4750 - accuracy: 0.8750 - val_loss: 0.6097 - val_accuracy: 0.6667\n",
      "Epoch 215/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.3835 - accuracy: 0.8125\n",
      "Epoch 215: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 215: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 0.4711 - accuracy: 0.7917 - val_loss: 0.6114 - val_accuracy: 0.6667\n",
      "Epoch 216/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.4595 - accuracy: 0.8750\n",
      "Epoch 216: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 216: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 50ms/step - loss: 0.3683 - accuracy: 0.9167 - val_loss: 0.6109 - val_accuracy: 0.6667\n",
      "Epoch 217/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.3422 - accuracy: 0.9375\n",
      "Epoch 217: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 217: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 51ms/step - loss: 0.3108 - accuracy: 0.9167 - val_loss: 0.6141 - val_accuracy: 0.6667\n",
      "Epoch 218/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.5243 - accuracy: 0.7500\n",
      "Epoch 218: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 218: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 35ms/step - loss: 0.4387 - accuracy: 0.8333 - val_loss: 0.6150 - val_accuracy: 0.6667\n",
      "Epoch 219/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.3697 - accuracy: 0.9375\n",
      "Epoch 219: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 219: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 28ms/step - loss: 0.3949 - accuracy: 0.8750 - val_loss: 0.6130 - val_accuracy: 0.6667\n",
      "Epoch 220/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.5070 - accuracy: 0.8125\n",
      "Epoch 220: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 220: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 28ms/step - loss: 0.5919 - accuracy: 0.7917 - val_loss: 0.6134 - val_accuracy: 0.6667\n",
      "Epoch 221/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.2814 - accuracy: 0.8750\n",
      "Epoch 221: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 221: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 39ms/step - loss: 0.3927 - accuracy: 0.8333 - val_loss: 0.6174 - val_accuracy: 0.6667\n",
      "Epoch 222/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.2354 - accuracy: 1.0000\n",
      "Epoch 222: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 222: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 33ms/step - loss: 0.3033 - accuracy: 0.9167 - val_loss: 0.6216 - val_accuracy: 0.6667\n",
      "Epoch 223/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.3356 - accuracy: 0.8750\n",
      "Epoch 223: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 223: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 34ms/step - loss: 0.3299 - accuracy: 0.8750 - val_loss: 0.6265 - val_accuracy: 0.6667\n",
      "Epoch 224/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.2442 - accuracy: 1.0000\n",
      "Epoch 224: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 224: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 30ms/step - loss: 0.3070 - accuracy: 0.9583 - val_loss: 0.6297 - val_accuracy: 0.6667\n",
      "Epoch 225/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.3956 - accuracy: 0.8125\n",
      "Epoch 225: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 225: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 30ms/step - loss: 0.3489 - accuracy: 0.8750 - val_loss: 0.6361 - val_accuracy: 0.6667\n",
      "Epoch 226/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.5557 - accuracy: 0.7500\n",
      "Epoch 226: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 226: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 0.4444 - accuracy: 0.8333 - val_loss: 0.6363 - val_accuracy: 0.6667\n",
      "Epoch 227/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.3656 - accuracy: 0.8750\n",
      "Epoch 227: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 227: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 32ms/step - loss: 0.3183 - accuracy: 0.8750 - val_loss: 0.6401 - val_accuracy: 0.6667\n",
      "Epoch 228/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.1485 - accuracy: 1.0000\n",
      "Epoch 228: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 228: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 35ms/step - loss: 0.2811 - accuracy: 0.9167 - val_loss: 0.6404 - val_accuracy: 0.6667\n",
      "Epoch 229/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.2741 - accuracy: 0.9375\n",
      "Epoch 229: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 229: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 28ms/step - loss: 0.3080 - accuracy: 0.9167 - val_loss: 0.6438 - val_accuracy: 0.6667\n",
      "Epoch 230/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.3037 - accuracy: 0.9375\n",
      "Epoch 230: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 230: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 0.2519 - accuracy: 0.9583 - val_loss: 0.6432 - val_accuracy: 0.6667\n",
      "Epoch 231/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.4278 - accuracy: 0.8750\n",
      "Epoch 231: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 231: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 33ms/step - loss: 0.3710 - accuracy: 0.9167 - val_loss: 0.6427 - val_accuracy: 0.6667\n",
      "Epoch 232/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.4175 - accuracy: 0.8750\n",
      "Epoch 232: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 232: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 28ms/step - loss: 0.3717 - accuracy: 0.8750 - val_loss: 0.6474 - val_accuracy: 0.6667\n",
      "Epoch 233/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.3128 - accuracy: 0.8750\n",
      "Epoch 233: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 233: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 28ms/step - loss: 0.5569 - accuracy: 0.7917 - val_loss: 0.6476 - val_accuracy: 0.6667\n",
      "Epoch 234/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.2495 - accuracy: 1.0000\n",
      "Epoch 234: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 234: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 41ms/step - loss: 0.4403 - accuracy: 0.9167 - val_loss: 0.6494 - val_accuracy: 0.6667\n",
      "Epoch 235/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.4417 - accuracy: 0.8125\n",
      "Epoch 235: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 235: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 32ms/step - loss: 0.3598 - accuracy: 0.8333 - val_loss: 0.6521 - val_accuracy: 0.6667\n",
      "Epoch 236/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.3189 - accuracy: 0.8750\n",
      "Epoch 236: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 236: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 28ms/step - loss: 0.2892 - accuracy: 0.9167 - val_loss: 0.6568 - val_accuracy: 0.6667\n",
      "Epoch 237/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.3959 - accuracy: 0.8750\n",
      "Epoch 237: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 237: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 30ms/step - loss: 0.3405 - accuracy: 0.9167 - val_loss: 0.6590 - val_accuracy: 0.6667\n",
      "Epoch 238/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.3341 - accuracy: 0.9375\n",
      "Epoch 238: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 238: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 28ms/step - loss: 0.3770 - accuracy: 0.9167 - val_loss: 0.6624 - val_accuracy: 0.6667\n",
      "Epoch 239/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.6173 - accuracy: 0.6875\n",
      "Epoch 239: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 239: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 0.5520 - accuracy: 0.7500 - val_loss: 0.6663 - val_accuracy: 0.6667\n",
      "Epoch 240/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.2785 - accuracy: 1.0000\n",
      "Epoch 240: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 240: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 0.3040 - accuracy: 0.9583 - val_loss: 0.6656 - val_accuracy: 0.6667\n",
      "Epoch 241/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.2776 - accuracy: 0.9375\n",
      "Epoch 241: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 241: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 42ms/step - loss: 0.3063 - accuracy: 0.9167 - val_loss: 0.6651 - val_accuracy: 0.6667\n",
      "Epoch 242/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.3651 - accuracy: 0.9375\n",
      "Epoch 242: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 242: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 30ms/step - loss: 0.3930 - accuracy: 0.8750 - val_loss: 0.6660 - val_accuracy: 0.6667\n",
      "Epoch 243/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.2263 - accuracy: 0.9375\n",
      "Epoch 243: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 243: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 30ms/step - loss: 0.3410 - accuracy: 0.8750 - val_loss: 0.6726 - val_accuracy: 0.6667\n",
      "Epoch 244/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.3972 - accuracy: 0.9375\n",
      "Epoch 244: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 244: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 0.4063 - accuracy: 0.9167 - val_loss: 0.6778 - val_accuracy: 0.6667\n",
      "Epoch 245/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.4561 - accuracy: 0.8750\n",
      "Epoch 245: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 245: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 28ms/step - loss: 0.4128 - accuracy: 0.8750 - val_loss: 0.6803 - val_accuracy: 0.6667\n",
      "Epoch 246/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.1900 - accuracy: 1.0000\n",
      "Epoch 246: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 246: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 28ms/step - loss: 0.2654 - accuracy: 0.9583 - val_loss: 0.6869 - val_accuracy: 0.5000\n",
      "Epoch 247/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.3721 - accuracy: 0.9375\n",
      "Epoch 247: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 247: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 55ms/step - loss: 0.3828 - accuracy: 0.8750 - val_loss: 0.6926 - val_accuracy: 0.5000\n",
      "Epoch 248/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.2003 - accuracy: 0.9375\n",
      "Epoch 248: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 248: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 29ms/step - loss: 0.2613 - accuracy: 0.9167 - val_loss: 0.6937 - val_accuracy: 0.5000\n",
      "Epoch 249/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.2357 - accuracy: 0.9375\n",
      "Epoch 249: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 249: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 0.2234 - accuracy: 0.9583 - val_loss: 0.6969 - val_accuracy: 0.5000\n",
      "Epoch 250/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.4573 - accuracy: 0.7500\n",
      "Epoch 250: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 250: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 29ms/step - loss: 0.5318 - accuracy: 0.7083 - val_loss: 0.6998 - val_accuracy: 0.5000\n",
      "Epoch 251/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.2837 - accuracy: 0.8125\n",
      "Epoch 251: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 251: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 0.3372 - accuracy: 0.8333 - val_loss: 0.6985 - val_accuracy: 0.5000\n",
      "Epoch 252/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.2188 - accuracy: 1.0000\n",
      "Epoch 252: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 252: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 0.2382 - accuracy: 1.0000 - val_loss: 0.7003 - val_accuracy: 0.5000\n",
      "Epoch 253/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.3795 - accuracy: 0.9375\n",
      "Epoch 253: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 253: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 28ms/step - loss: 0.4338 - accuracy: 0.8750 - val_loss: 0.6974 - val_accuracy: 0.5000\n",
      "Epoch 254/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.2538 - accuracy: 0.8750\n",
      "Epoch 254: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 254: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 85ms/step - loss: 0.3518 - accuracy: 0.8333 - val_loss: 0.6959 - val_accuracy: 0.5000\n",
      "Epoch 255/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.4917 - accuracy: 0.8750\n",
      "Epoch 255: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 255: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 28ms/step - loss: 0.4097 - accuracy: 0.8750 - val_loss: 0.6995 - val_accuracy: 0.5000\n",
      "Epoch 256/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.5484 - accuracy: 0.8125\n",
      "Epoch 256: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 256: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 30ms/step - loss: 0.5264 - accuracy: 0.8333 - val_loss: 0.6989 - val_accuracy: 0.5000\n",
      "Epoch 257/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.1587 - accuracy: 1.0000\n",
      "Epoch 257: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 257: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 0.3063 - accuracy: 0.8333 - val_loss: 0.6995 - val_accuracy: 0.5000\n",
      "Epoch 258/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.3357 - accuracy: 0.8750\n",
      "Epoch 258: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 258: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 32ms/step - loss: 0.4765 - accuracy: 0.8750 - val_loss: 0.6967 - val_accuracy: 0.5000\n",
      "Epoch 259/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.2435 - accuracy: 1.0000\n",
      "Epoch 259: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 259: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 45ms/step - loss: 0.3368 - accuracy: 0.9167 - val_loss: 0.6965 - val_accuracy: 0.5000\n",
      "Epoch 260/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.4905 - accuracy: 0.8125\n",
      "Epoch 260: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 260: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 29ms/step - loss: 0.5405 - accuracy: 0.7500 - val_loss: 0.6997 - val_accuracy: 0.5000\n",
      "Epoch 261/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.2270 - accuracy: 1.0000\n",
      "Epoch 261: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 261: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 0.3285 - accuracy: 0.9583 - val_loss: 0.6955 - val_accuracy: 0.5000\n",
      "Epoch 262/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.4522 - accuracy: 0.8750\n",
      "Epoch 262: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 262: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 28ms/step - loss: 0.3898 - accuracy: 0.8333 - val_loss: 0.6988 - val_accuracy: 0.5000\n",
      "Epoch 263/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.4895 - accuracy: 0.7500\n",
      "Epoch 263: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 263: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 32ms/step - loss: 0.4837 - accuracy: 0.7917 - val_loss: 0.6990 - val_accuracy: 0.5000\n",
      "Epoch 264/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.2556 - accuracy: 0.9375\n",
      "Epoch 264: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 264: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 0.2231 - accuracy: 0.9583 - val_loss: 0.7025 - val_accuracy: 0.5000\n",
      "Epoch 265/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.3411 - accuracy: 0.8750\n",
      "Epoch 265: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 265: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 28ms/step - loss: 0.3187 - accuracy: 0.9167 - val_loss: 0.7017 - val_accuracy: 0.5000\n",
      "Epoch 266/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.5665 - accuracy: 0.6875\n",
      "Epoch 266: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 266: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 0.4709 - accuracy: 0.7500 - val_loss: 0.7026 - val_accuracy: 0.5000\n",
      "Epoch 267/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.2870 - accuracy: 1.0000\n",
      "Epoch 267: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 267: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 48ms/step - loss: 0.2845 - accuracy: 0.9583 - val_loss: 0.7046 - val_accuracy: 0.5000\n",
      "Epoch 268/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.2618 - accuracy: 0.8750\n",
      "Epoch 268: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 268: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 0.2871 - accuracy: 0.8750 - val_loss: 0.7098 - val_accuracy: 0.5000\n",
      "Epoch 269/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.2712 - accuracy: 0.9375\n",
      "Epoch 269: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 269: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 34ms/step - loss: 0.3081 - accuracy: 0.9167 - val_loss: 0.7101 - val_accuracy: 0.5000\n",
      "Epoch 270/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.4724 - accuracy: 0.7500\n",
      "Epoch 270: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 270: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 0.6427 - accuracy: 0.6667 - val_loss: 0.7106 - val_accuracy: 0.5000\n",
      "Epoch 271/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.3505 - accuracy: 0.9375\n",
      "Epoch 271: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 271: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 28ms/step - loss: 0.3062 - accuracy: 0.9167 - val_loss: 0.7156 - val_accuracy: 0.5000\n",
      "Epoch 272/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.2926 - accuracy: 0.9375\n",
      "Epoch 272: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 272: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 0.3147 - accuracy: 0.8750 - val_loss: 0.7181 - val_accuracy: 0.5000\n",
      "Epoch 273/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.4260 - accuracy: 0.8125\n",
      "Epoch 273: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 273: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 31ms/step - loss: 0.3582 - accuracy: 0.8750 - val_loss: 0.7181 - val_accuracy: 0.5000\n",
      "Epoch 274/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.2642 - accuracy: 0.9375\n",
      "Epoch 274: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 274: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 45ms/step - loss: 0.2986 - accuracy: 0.9167 - val_loss: 0.7198 - val_accuracy: 0.5000\n",
      "Epoch 275/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.6480 - accuracy: 0.7500\n",
      "Epoch 275: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 275: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 32ms/step - loss: 0.6061 - accuracy: 0.7083 - val_loss: 0.7197 - val_accuracy: 0.5000\n",
      "Epoch 276/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.3743 - accuracy: 0.8125\n",
      "Epoch 276: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 276: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 29ms/step - loss: 0.2784 - accuracy: 0.8750 - val_loss: 0.7236 - val_accuracy: 0.5000\n",
      "Epoch 277/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.2060 - accuracy: 0.8750\n",
      "Epoch 277: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 277: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 0.1880 - accuracy: 0.9167 - val_loss: 0.7266 - val_accuracy: 0.5000\n",
      "Epoch 278/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.3910 - accuracy: 0.9375\n",
      "Epoch 278: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 278: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 40ms/step - loss: 0.3298 - accuracy: 0.9583 - val_loss: 0.7285 - val_accuracy: 0.5000\n",
      "Epoch 279/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.4609 - accuracy: 0.8750\n",
      "Epoch 279: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 279: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 30ms/step - loss: 0.4662 - accuracy: 0.7917 - val_loss: 0.7258 - val_accuracy: 0.5000\n",
      "Epoch 280/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.3348 - accuracy: 0.9375\n",
      "Epoch 280: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 280: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 35ms/step - loss: 0.2702 - accuracy: 0.9583 - val_loss: 0.7275 - val_accuracy: 0.5000\n",
      "Epoch 281/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.3304 - accuracy: 0.8125\n",
      "Epoch 281: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 281: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 61ms/step - loss: 0.3239 - accuracy: 0.8333 - val_loss: 0.7306 - val_accuracy: 0.5000\n",
      "Epoch 282/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.2460 - accuracy: 0.9375\n",
      "Epoch 282: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 282: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 33ms/step - loss: 0.2983 - accuracy: 0.8750 - val_loss: 0.7330 - val_accuracy: 0.5000\n",
      "Epoch 283/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.3248 - accuracy: 0.9375\n",
      "Epoch 283: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 283: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 34ms/step - loss: 0.3224 - accuracy: 0.9167 - val_loss: 0.7374 - val_accuracy: 0.5000\n",
      "Epoch 284/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.6448 - accuracy: 0.7500\n",
      "Epoch 284: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 284: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 42ms/step - loss: 0.4755 - accuracy: 0.8333 - val_loss: 0.7411 - val_accuracy: 0.5000\n",
      "Epoch 285/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.4320 - accuracy: 0.8125\n",
      "Epoch 285: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 285: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 35ms/step - loss: 0.3364 - accuracy: 0.8750 - val_loss: 0.7464 - val_accuracy: 0.5000\n",
      "Epoch 286/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.5829 - accuracy: 0.8125\n",
      "Epoch 286: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 286: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 34ms/step - loss: 0.4732 - accuracy: 0.8750 - val_loss: 0.7547 - val_accuracy: 0.5000\n",
      "Epoch 287/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.3643 - accuracy: 0.8125\n",
      "Epoch 287: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 287: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 44ms/step - loss: 0.4063 - accuracy: 0.7917 - val_loss: 0.7578 - val_accuracy: 0.5000\n",
      "Epoch 288/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.3427 - accuracy: 0.9375\n",
      "Epoch 288: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 288: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 87ms/step - loss: 0.3062 - accuracy: 0.9583 - val_loss: 0.7606 - val_accuracy: 0.5000\n",
      "Epoch 289/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.3487 - accuracy: 0.8125\n",
      "Epoch 289: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 289: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 39ms/step - loss: 0.3086 - accuracy: 0.8750 - val_loss: 0.7637 - val_accuracy: 0.5000\n",
      "Epoch 290/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.2567 - accuracy: 0.8750\n",
      "Epoch 290: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 290: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 34ms/step - loss: 0.3567 - accuracy: 0.8333 - val_loss: 0.7684 - val_accuracy: 0.5000\n",
      "Epoch 291/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.1969 - accuracy: 1.0000\n",
      "Epoch 291: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 291: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 31ms/step - loss: 0.2653 - accuracy: 0.9583 - val_loss: 0.7683 - val_accuracy: 0.5000\n",
      "Epoch 292/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.3836 - accuracy: 0.8750\n",
      "Epoch 292: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 292: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 28ms/step - loss: 0.3488 - accuracy: 0.8750 - val_loss: 0.7700 - val_accuracy: 0.5000\n",
      "Epoch 293/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.2567 - accuracy: 0.9375\n",
      "Epoch 293: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 293: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 64ms/step - loss: 0.4485 - accuracy: 0.8333 - val_loss: 0.7717 - val_accuracy: 0.5000\n",
      "Epoch 294/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.3850 - accuracy: 0.8125\n",
      "Epoch 294: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 294: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 31ms/step - loss: 0.2986 - accuracy: 0.8750 - val_loss: 0.7742 - val_accuracy: 0.5000\n",
      "Epoch 295/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.3033 - accuracy: 0.8750\n",
      "Epoch 295: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 295: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 33ms/step - loss: 0.2569 - accuracy: 0.9167 - val_loss: 0.7708 - val_accuracy: 0.5000\n",
      "Epoch 296/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.2320 - accuracy: 0.9375\n",
      "Epoch 296: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 296: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 30ms/step - loss: 0.3449 - accuracy: 0.8750 - val_loss: 0.7719 - val_accuracy: 0.5000\n",
      "Epoch 297/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.1038 - accuracy: 1.0000\n",
      "Epoch 297: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 297: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 0.1497 - accuracy: 1.0000 - val_loss: 0.7758 - val_accuracy: 0.5000\n",
      "Epoch 298/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.5198 - accuracy: 0.8125\n",
      "Epoch 298: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 298: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 28ms/step - loss: 0.4066 - accuracy: 0.8750 - val_loss: 0.7748 - val_accuracy: 0.5000\n",
      "Epoch 299/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.2619 - accuracy: 0.9375\n",
      "Epoch 299: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 299: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 52ms/step - loss: 0.7435 - accuracy: 0.7500 - val_loss: 0.7706 - val_accuracy: 0.5000\n",
      "Epoch 300/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.3376 - accuracy: 0.8750\n",
      "Epoch 300: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 300: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 28ms/step - loss: 0.2737 - accuracy: 0.9167 - val_loss: 0.7747 - val_accuracy: 0.5000\n",
      "Epoch 301/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.2651 - accuracy: 0.9375\n",
      "Epoch 301: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 301: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 29ms/step - loss: 0.2171 - accuracy: 0.9583 - val_loss: 0.7798 - val_accuracy: 0.5000\n",
      "Epoch 302/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.2125 - accuracy: 0.9375\n",
      "Epoch 302: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 302: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 28ms/step - loss: 0.2925 - accuracy: 0.9167 - val_loss: 0.7804 - val_accuracy: 0.5000\n",
      "Epoch 303/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.3221 - accuracy: 0.8750\n",
      "Epoch 303: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 303: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 30ms/step - loss: 0.2595 - accuracy: 0.9167 - val_loss: 0.7872 - val_accuracy: 0.5000\n",
      "Epoch 304/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.3795 - accuracy: 0.8750\n",
      "Epoch 304: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 304: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 31ms/step - loss: 0.5611 - accuracy: 0.8333 - val_loss: 0.7879 - val_accuracy: 0.5000\n",
      "Epoch 305/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.3704 - accuracy: 0.8750\n",
      "Epoch 305: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 305: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 53ms/step - loss: 0.3518 - accuracy: 0.8750 - val_loss: 0.7955 - val_accuracy: 0.5000\n",
      "Epoch 306/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.3058 - accuracy: 0.8750\n",
      "Epoch 306: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 306: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 33ms/step - loss: 0.3882 - accuracy: 0.8333 - val_loss: 0.8017 - val_accuracy: 0.5000\n",
      "Epoch 307/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.4249 - accuracy: 0.8125\n",
      "Epoch 307: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 307: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 48ms/step - loss: 0.3401 - accuracy: 0.8750 - val_loss: 0.8063 - val_accuracy: 0.5000\n",
      "Epoch 308/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.2401 - accuracy: 0.9375\n",
      "Epoch 308: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 308: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 28ms/step - loss: 0.4488 - accuracy: 0.8333 - val_loss: 0.8060 - val_accuracy: 0.5000\n",
      "Epoch 309/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.4131 - accuracy: 0.8750\n",
      "Epoch 309: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 309: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 0.3493 - accuracy: 0.9167 - val_loss: 0.8100 - val_accuracy: 0.5000\n",
      "Epoch 310/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.3224 - accuracy: 0.8750\n",
      "Epoch 310: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 310: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 28ms/step - loss: 0.3969 - accuracy: 0.8333 - val_loss: 0.8118 - val_accuracy: 0.5000\n",
      "Epoch 311/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.4096 - accuracy: 0.8750\n",
      "Epoch 311: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 311: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 0.3219 - accuracy: 0.8750 - val_loss: 0.8136 - val_accuracy: 0.5000\n",
      "Epoch 312/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.1281 - accuracy: 1.0000\n",
      "Epoch 312: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 312: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 48ms/step - loss: 0.2931 - accuracy: 0.8750 - val_loss: 0.8138 - val_accuracy: 0.5000\n",
      "Epoch 313/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.2280 - accuracy: 1.0000\n",
      "Epoch 313: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 313: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 28ms/step - loss: 0.1971 - accuracy: 1.0000 - val_loss: 0.8149 - val_accuracy: 0.5000\n",
      "Epoch 314/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.4015 - accuracy: 0.8750\n",
      "Epoch 314: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 314: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 30ms/step - loss: 0.3754 - accuracy: 0.9167 - val_loss: 0.8170 - val_accuracy: 0.5000\n",
      "Epoch 315/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.3609 - accuracy: 0.8750\n",
      "Epoch 315: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 315: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 31ms/step - loss: 0.2975 - accuracy: 0.9167 - val_loss: 0.8163 - val_accuracy: 0.5000\n",
      "Epoch 316/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.2875 - accuracy: 0.8750\n",
      "Epoch 316: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 316: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 0.3177 - accuracy: 0.8750 - val_loss: 0.8201 - val_accuracy: 0.5000\n",
      "Epoch 317/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.5041 - accuracy: 0.8750\n",
      "Epoch 317: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 317: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 32ms/step - loss: 0.4946 - accuracy: 0.8750 - val_loss: 0.8189 - val_accuracy: 0.5000\n",
      "Epoch 318/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.3107 - accuracy: 0.8750\n",
      "Epoch 318: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 318: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 29ms/step - loss: 0.4227 - accuracy: 0.8333 - val_loss: 0.8207 - val_accuracy: 0.5000\n",
      "Epoch 319/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.3134 - accuracy: 0.9375\n",
      "Epoch 319: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 319: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 42ms/step - loss: 0.3386 - accuracy: 0.9167 - val_loss: 0.8255 - val_accuracy: 0.5000\n",
      "Epoch 320/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.7689 - accuracy: 0.5625\n",
      "Epoch 320: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 320: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 29ms/step - loss: 0.6523 - accuracy: 0.6667 - val_loss: 0.8310 - val_accuracy: 0.5000\n",
      "Epoch 321/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.4557 - accuracy: 0.8125\n",
      "Epoch 321: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 321: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 0.4013 - accuracy: 0.8333 - val_loss: 0.8322 - val_accuracy: 0.5000\n",
      "Epoch 322/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.1530 - accuracy: 0.9375\n",
      "Epoch 322: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 322: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 30ms/step - loss: 0.2186 - accuracy: 0.9583 - val_loss: 0.8334 - val_accuracy: 0.5000\n",
      "Epoch 323/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.4054 - accuracy: 0.8750\n",
      "Epoch 323: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 323: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 29ms/step - loss: 0.3836 - accuracy: 0.8333 - val_loss: 0.8349 - val_accuracy: 0.5000\n",
      "Epoch 324/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.3774 - accuracy: 0.8750\n",
      "Epoch 324: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 324: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 0.3980 - accuracy: 0.8333 - val_loss: 0.8375 - val_accuracy: 0.5000\n",
      "Epoch 325/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.1832 - accuracy: 0.9375\n",
      "Epoch 325: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 325: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 41ms/step - loss: 0.2669 - accuracy: 0.9167 - val_loss: 0.8393 - val_accuracy: 0.5000\n",
      "Epoch 326/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.1563 - accuracy: 1.0000\n",
      "Epoch 326: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 326: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 48ms/step - loss: 0.2422 - accuracy: 0.9583 - val_loss: 0.8448 - val_accuracy: 0.5000\n",
      "Epoch 327/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.1415 - accuracy: 1.0000\n",
      "Epoch 327: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 327: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 0.2227 - accuracy: 0.9167 - val_loss: 0.8417 - val_accuracy: 0.5000\n",
      "Epoch 328/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.2327 - accuracy: 0.8125\n",
      "Epoch 328: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 328: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 30ms/step - loss: 0.2573 - accuracy: 0.8333 - val_loss: 0.8440 - val_accuracy: 0.5000\n",
      "Epoch 329/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.4868 - accuracy: 0.8125\n",
      "Epoch 329: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 329: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 0.4956 - accuracy: 0.8333 - val_loss: 0.8506 - val_accuracy: 0.5000\n",
      "Epoch 330/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.3547 - accuracy: 0.8125\n",
      "Epoch 330: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 330: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 0.4102 - accuracy: 0.7917 - val_loss: 0.8509 - val_accuracy: 0.5000\n",
      "Epoch 331/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.2738 - accuracy: 1.0000\n",
      "Epoch 331: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 331: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 28ms/step - loss: 0.2708 - accuracy: 1.0000 - val_loss: 0.8544 - val_accuracy: 0.5000\n",
      "Epoch 332/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.3624 - accuracy: 0.9375\n",
      "Epoch 332: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 332: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 34ms/step - loss: 0.4231 - accuracy: 0.9167 - val_loss: 0.8530 - val_accuracy: 0.5000\n",
      "Epoch 333/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.3882 - accuracy: 0.8750\n",
      "Epoch 333: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 333: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 28ms/step - loss: 0.3455 - accuracy: 0.9167 - val_loss: 0.8544 - val_accuracy: 0.5000\n",
      "Epoch 334/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.4112 - accuracy: 0.8125\n",
      "Epoch 334: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 334: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 28ms/step - loss: 0.4221 - accuracy: 0.7917 - val_loss: 0.8541 - val_accuracy: 0.5000\n",
      "Epoch 335/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.3280 - accuracy: 0.9375\n",
      "Epoch 335: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 335: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 0.3224 - accuracy: 0.9167 - val_loss: 0.8607 - val_accuracy: 0.5000\n",
      "Epoch 336/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.3905 - accuracy: 0.8750\n",
      "Epoch 336: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 336: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 0.2869 - accuracy: 0.9167 - val_loss: 0.8674 - val_accuracy: 0.5000\n",
      "Epoch 337/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.4218 - accuracy: 0.8750\n",
      "Epoch 337: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 337: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 29ms/step - loss: 0.3802 - accuracy: 0.8750 - val_loss: 0.8725 - val_accuracy: 0.5000\n",
      "Epoch 338/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.1910 - accuracy: 1.0000\n",
      "Epoch 338: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 338: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 31ms/step - loss: 0.2678 - accuracy: 0.9583 - val_loss: 0.8745 - val_accuracy: 0.5000\n",
      "Epoch 339/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.3441 - accuracy: 0.8750\n",
      "Epoch 339: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 339: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 52ms/step - loss: 0.2578 - accuracy: 0.9167 - val_loss: 0.8767 - val_accuracy: 0.5000\n",
      "Epoch 340/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.2441 - accuracy: 0.9375\n",
      "Epoch 340: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 340: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 30ms/step - loss: 0.2710 - accuracy: 0.9167 - val_loss: 0.8764 - val_accuracy: 0.5000\n",
      "Epoch 341/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.2793 - accuracy: 0.8750\n",
      "Epoch 341: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 341: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 28ms/step - loss: 0.2683 - accuracy: 0.9167 - val_loss: 0.8778 - val_accuracy: 0.5000\n",
      "Epoch 342/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.1885 - accuracy: 1.0000\n",
      "Epoch 342: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 342: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 30ms/step - loss: 0.3136 - accuracy: 0.8750 - val_loss: 0.8773 - val_accuracy: 0.5000\n",
      "Epoch 343/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.5479 - accuracy: 0.6250\n",
      "Epoch 343: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 343: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 0.3929 - accuracy: 0.7500 - val_loss: 0.8815 - val_accuracy: 0.5000\n",
      "Epoch 344/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.3653 - accuracy: 0.8750\n",
      "Epoch 344: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 344: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 29ms/step - loss: 0.3321 - accuracy: 0.9167 - val_loss: 0.8828 - val_accuracy: 0.5000\n",
      "Epoch 345/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.1445 - accuracy: 1.0000\n",
      "Epoch 345: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 345: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 43ms/step - loss: 0.1711 - accuracy: 0.9583 - val_loss: 0.8834 - val_accuracy: 0.3333\n",
      "Epoch 346/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.6681 - accuracy: 0.7500\n",
      "Epoch 346: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 346: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 29ms/step - loss: 0.7257 - accuracy: 0.7083 - val_loss: 0.8907 - val_accuracy: 0.3333\n",
      "Epoch 347/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.2341 - accuracy: 1.0000\n",
      "Epoch 347: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 347: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 0.2438 - accuracy: 0.9583 - val_loss: 0.8883 - val_accuracy: 0.3333\n",
      "Epoch 348/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.3304 - accuracy: 0.8750\n",
      "Epoch 348: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 348: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 0.3335 - accuracy: 0.8750 - val_loss: 0.8902 - val_accuracy: 0.3333\n",
      "Epoch 349/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.3206 - accuracy: 0.8750\n",
      "Epoch 349: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 349: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 30ms/step - loss: 0.3519 - accuracy: 0.8750 - val_loss: 0.8896 - val_accuracy: 0.3333\n",
      "Epoch 350/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.4233 - accuracy: 0.7500\n",
      "Epoch 350: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 350: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 0.3226 - accuracy: 0.8333 - val_loss: 0.8900 - val_accuracy: 0.3333\n",
      "Epoch 351/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.3531 - accuracy: 0.9375\n",
      "Epoch 351: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 351: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 28ms/step - loss: 0.4251 - accuracy: 0.8750 - val_loss: 0.8894 - val_accuracy: 0.3333\n",
      "Epoch 352/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.4002 - accuracy: 0.8125\n",
      "Epoch 352: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 352: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 28ms/step - loss: 0.3772 - accuracy: 0.8333 - val_loss: 0.8918 - val_accuracy: 0.3333\n",
      "Epoch 353/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.3900 - accuracy: 0.9375\n",
      "Epoch 353: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 353: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 38ms/step - loss: 0.3995 - accuracy: 0.9167 - val_loss: 0.8912 - val_accuracy: 0.5000\n",
      "Epoch 354/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.3067 - accuracy: 0.8750\n",
      "Epoch 354: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 354: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 0.3722 - accuracy: 0.8333 - val_loss: 0.8888 - val_accuracy: 0.5000\n",
      "Epoch 355/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.1937 - accuracy: 1.0000\n",
      "Epoch 355: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 355: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 30ms/step - loss: 0.2372 - accuracy: 0.9583 - val_loss: 0.8894 - val_accuracy: 0.5000\n",
      "Epoch 356/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.3053 - accuracy: 0.9375\n",
      "Epoch 356: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 356: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 29ms/step - loss: 0.2492 - accuracy: 0.9583 - val_loss: 0.8906 - val_accuracy: 0.5000\n",
      "Epoch 357/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.1223 - accuracy: 1.0000\n",
      "Epoch 357: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 357: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 0.2178 - accuracy: 0.9167 - val_loss: 0.8925 - val_accuracy: 0.5000\n",
      "Epoch 358/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.2491 - accuracy: 0.9375\n",
      "Epoch 358: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 358: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 45ms/step - loss: 0.2522 - accuracy: 0.9167 - val_loss: 0.8924 - val_accuracy: 0.5000\n",
      "Epoch 359/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.3921 - accuracy: 0.9375\n",
      "Epoch 359: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 359: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 30ms/step - loss: 0.4099 - accuracy: 0.8750 - val_loss: 0.8907 - val_accuracy: 0.5000\n",
      "Epoch 360/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.1917 - accuracy: 1.0000\n",
      "Epoch 360: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 360: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 28ms/step - loss: 0.2684 - accuracy: 0.9583 - val_loss: 0.8929 - val_accuracy: 0.5000\n",
      "Epoch 361/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.1702 - accuracy: 0.9375\n",
      "Epoch 361: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 361: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 0.2475 - accuracy: 0.8750 - val_loss: 0.8969 - val_accuracy: 0.5000\n",
      "Epoch 362/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.3407 - accuracy: 0.8750\n",
      "Epoch 362: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 362: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 30ms/step - loss: 0.3668 - accuracy: 0.8750 - val_loss: 0.8983 - val_accuracy: 0.5000\n",
      "Epoch 363/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.2626 - accuracy: 0.9375\n",
      "Epoch 363: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 363: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 28ms/step - loss: 0.3471 - accuracy: 0.9167 - val_loss: 0.8999 - val_accuracy: 0.5000\n",
      "Epoch 364/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.4100 - accuracy: 0.8750\n",
      "Epoch 364: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 364: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 47ms/step - loss: 0.3175 - accuracy: 0.9167 - val_loss: 0.9053 - val_accuracy: 0.5000\n",
      "Epoch 365/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.4259 - accuracy: 0.7500\n",
      "Epoch 365: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 365: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 28ms/step - loss: 0.4528 - accuracy: 0.7083 - val_loss: 0.9111 - val_accuracy: 0.5000\n",
      "Epoch 366/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.4104 - accuracy: 0.7500\n",
      "Epoch 366: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 366: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 31ms/step - loss: 0.3423 - accuracy: 0.8333 - val_loss: 0.9097 - val_accuracy: 0.5000\n",
      "Epoch 367/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.2016 - accuracy: 0.9375\n",
      "Epoch 367: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 367: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 30ms/step - loss: 0.2299 - accuracy: 0.9167 - val_loss: 0.9129 - val_accuracy: 0.5000\n",
      "Epoch 368/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.2902 - accuracy: 0.8750\n",
      "Epoch 368: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 368: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 0.2264 - accuracy: 0.9167 - val_loss: 0.9176 - val_accuracy: 0.5000\n",
      "Epoch 369/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.2734 - accuracy: 0.9375\n",
      "Epoch 369: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 369: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 29ms/step - loss: 0.2623 - accuracy: 0.9583 - val_loss: 0.9163 - val_accuracy: 0.5000\n",
      "Epoch 370/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.2227 - accuracy: 0.9375\n",
      "Epoch 370: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 370: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 42ms/step - loss: 0.2068 - accuracy: 0.9583 - val_loss: 0.9165 - val_accuracy: 0.5000\n",
      "Epoch 371/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.3263 - accuracy: 0.8750\n",
      "Epoch 371: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 371: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 30ms/step - loss: 0.2972 - accuracy: 0.9167 - val_loss: 0.9214 - val_accuracy: 0.5000\n",
      "Epoch 372/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.4312 - accuracy: 0.6875\n",
      "Epoch 372: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 372: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 28ms/step - loss: 0.3903 - accuracy: 0.7500 - val_loss: 0.9240 - val_accuracy: 0.5000\n",
      "Epoch 373/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.1988 - accuracy: 1.0000\n",
      "Epoch 373: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 373: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 33ms/step - loss: 0.1850 - accuracy: 1.0000 - val_loss: 0.9272 - val_accuracy: 0.5000\n",
      "Epoch 374/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.2913 - accuracy: 0.9375\n",
      "Epoch 374: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 374: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 0.3902 - accuracy: 0.9167 - val_loss: 0.9273 - val_accuracy: 0.5000\n",
      "Epoch 375/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.4543 - accuracy: 0.8750\n",
      "Epoch 375: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 375: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 0.3746 - accuracy: 0.9167 - val_loss: 0.9289 - val_accuracy: 0.5000\n",
      "Epoch 376/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.3683 - accuracy: 0.8750\n",
      "Epoch 376: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 376: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 47ms/step - loss: 0.4344 - accuracy: 0.8333 - val_loss: 0.9318 - val_accuracy: 0.5000\n",
      "Epoch 377/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.3662 - accuracy: 0.9375\n",
      "Epoch 377: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 377: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 31ms/step - loss: 0.3974 - accuracy: 0.8750 - val_loss: 0.9295 - val_accuracy: 0.5000\n",
      "Epoch 378/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.2823 - accuracy: 0.9375\n",
      "Epoch 378: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 378: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 0.2844 - accuracy: 0.9167 - val_loss: 0.9264 - val_accuracy: 0.5000\n",
      "Epoch 379/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.2699 - accuracy: 0.9375\n",
      "Epoch 379: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 379: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 0.2931 - accuracy: 0.9167 - val_loss: 0.9249 - val_accuracy: 0.5000\n",
      "Epoch 380/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.3017 - accuracy: 0.8750\n",
      "Epoch 380: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 380: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 0.2451 - accuracy: 0.9167 - val_loss: 0.9247 - val_accuracy: 0.5000\n",
      "Epoch 381/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.3300 - accuracy: 0.8750\n",
      "Epoch 381: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 381: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 31ms/step - loss: 0.2795 - accuracy: 0.9167 - val_loss: 0.9279 - val_accuracy: 0.5000\n",
      "Epoch 382/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.3911 - accuracy: 0.8750\n",
      "Epoch 382: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 382: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 42ms/step - loss: 0.4401 - accuracy: 0.8333 - val_loss: 0.9251 - val_accuracy: 0.5000\n",
      "Epoch 383/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.2180 - accuracy: 1.0000\n",
      "Epoch 383: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 383: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 28ms/step - loss: 0.1976 - accuracy: 1.0000 - val_loss: 0.9232 - val_accuracy: 0.5000\n",
      "Epoch 384/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.2061 - accuracy: 1.0000\n",
      "Epoch 384: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 384: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 30ms/step - loss: 0.2516 - accuracy: 0.9167 - val_loss: 0.9214 - val_accuracy: 0.5000\n",
      "Epoch 385/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.2582 - accuracy: 0.9375\n",
      "Epoch 385: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 385: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 0.2456 - accuracy: 0.9583 - val_loss: 0.9220 - val_accuracy: 0.5000\n",
      "Epoch 386/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.2523 - accuracy: 0.9375\n",
      "Epoch 386: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 386: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 30ms/step - loss: 0.3171 - accuracy: 0.8750 - val_loss: 0.9220 - val_accuracy: 0.5000\n",
      "Epoch 387/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.2882 - accuracy: 0.9375\n",
      "Epoch 387: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 387: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 0.2358 - accuracy: 0.9583 - val_loss: 0.9223 - val_accuracy: 0.5000\n",
      "Epoch 388/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.2989 - accuracy: 0.9375\n",
      "Epoch 388: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 388: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 29ms/step - loss: 0.2657 - accuracy: 0.9583 - val_loss: 0.9182 - val_accuracy: 0.5000\n",
      "Epoch 389/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.3030 - accuracy: 0.9375\n",
      "Epoch 389: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 389: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 31ms/step - loss: 0.2760 - accuracy: 0.9583 - val_loss: 0.9199 - val_accuracy: 0.5000\n",
      "Epoch 390/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.4534 - accuracy: 0.9375\n",
      "Epoch 390: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 390: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 29ms/step - loss: 0.3445 - accuracy: 0.9583 - val_loss: 0.9187 - val_accuracy: 0.5000\n",
      "Epoch 391/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.2027 - accuracy: 1.0000\n",
      "Epoch 391: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 391: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 0.1864 - accuracy: 1.0000 - val_loss: 0.9175 - val_accuracy: 0.5000\n",
      "Epoch 392/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.2550 - accuracy: 1.0000\n",
      "Epoch 392: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 392: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 29ms/step - loss: 0.3784 - accuracy: 0.9167 - val_loss: 0.9142 - val_accuracy: 0.5000\n",
      "Epoch 393/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.2604 - accuracy: 0.9375\n",
      "Epoch 393: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 393: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 0.4152 - accuracy: 0.8750 - val_loss: 0.9134 - val_accuracy: 0.5000\n",
      "Epoch 394/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.1368 - accuracy: 1.0000\n",
      "Epoch 394: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 394: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 61ms/step - loss: 0.1387 - accuracy: 1.0000 - val_loss: 0.9140 - val_accuracy: 0.5000\n",
      "Epoch 395/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.3258 - accuracy: 0.9375\n",
      "Epoch 395: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 395: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 28ms/step - loss: 0.3126 - accuracy: 0.8750 - val_loss: 0.9179 - val_accuracy: 0.5000\n",
      "Epoch 396/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.4265 - accuracy: 0.8125\n",
      "Epoch 396: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 396: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 0.3713 - accuracy: 0.8333 - val_loss: 0.9225 - val_accuracy: 0.5000\n",
      "Epoch 397/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.1721 - accuracy: 0.9375\n",
      "Epoch 397: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 397: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 56ms/step - loss: 0.1602 - accuracy: 0.9583 - val_loss: 0.9235 - val_accuracy: 0.5000\n",
      "Epoch 398/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.2356 - accuracy: 0.9375\n",
      "Epoch 398: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 398: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 0.2159 - accuracy: 0.9167 - val_loss: 0.9274 - val_accuracy: 0.5000\n",
      "Epoch 399/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.3037 - accuracy: 0.8750\n",
      "Epoch 399: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 399: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 51ms/step - loss: 0.2488 - accuracy: 0.9167 - val_loss: 0.9318 - val_accuracy: 0.5000\n",
      "Epoch 400/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.2803 - accuracy: 0.8750\n",
      "Epoch 400: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 400: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 28ms/step - loss: 0.3066 - accuracy: 0.8750 - val_loss: 0.9327 - val_accuracy: 0.5000\n",
      "Epoch 401/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.1880 - accuracy: 1.0000\n",
      "Epoch 401: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 401: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 0.1799 - accuracy: 1.0000 - val_loss: 0.9352 - val_accuracy: 0.5000\n",
      "Epoch 402/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.4288 - accuracy: 0.8125\n",
      "Epoch 402: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 402: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 35ms/step - loss: 0.4649 - accuracy: 0.8333 - val_loss: 0.9373 - val_accuracy: 0.3333\n",
      "Epoch 403/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.3694 - accuracy: 0.8750\n",
      "Epoch 403: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 403: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 34ms/step - loss: 0.3284 - accuracy: 0.8750 - val_loss: 0.9417 - val_accuracy: 0.3333\n",
      "Epoch 404/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.2839 - accuracy: 0.9375\n",
      "Epoch 404: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 404: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 42ms/step - loss: 0.3886 - accuracy: 0.8750 - val_loss: 0.9491 - val_accuracy: 0.3333\n",
      "Epoch 405/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.3186 - accuracy: 0.9375\n",
      "Epoch 405: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 405: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 40ms/step - loss: 0.4518 - accuracy: 0.8750 - val_loss: 0.9521 - val_accuracy: 0.3333\n",
      "Epoch 406/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.1742 - accuracy: 0.9375\n",
      "Epoch 406: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 406: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 29ms/step - loss: 0.1912 - accuracy: 0.9583 - val_loss: 0.9543 - val_accuracy: 0.3333\n",
      "Epoch 407/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.2657 - accuracy: 0.9375\n",
      "Epoch 407: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 407: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 28ms/step - loss: 0.2056 - accuracy: 0.9583 - val_loss: 0.9548 - val_accuracy: 0.3333\n",
      "Epoch 408/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.4136 - accuracy: 0.8750\n",
      "Epoch 408: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 408: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 33ms/step - loss: 0.3497 - accuracy: 0.9167 - val_loss: 0.9587 - val_accuracy: 0.3333\n",
      "Epoch 409/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.3738 - accuracy: 0.8750\n",
      "Epoch 409: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 409: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 55ms/step - loss: 0.3158 - accuracy: 0.9167 - val_loss: 0.9605 - val_accuracy: 0.3333\n",
      "Epoch 410/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.4212 - accuracy: 0.8125\n",
      "Epoch 410: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 410: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 55ms/step - loss: 0.3871 - accuracy: 0.7917 - val_loss: 0.9567 - val_accuracy: 0.3333\n",
      "Epoch 411/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.4664 - accuracy: 0.8125\n",
      "Epoch 411: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 411: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 37ms/step - loss: 0.3803 - accuracy: 0.8333 - val_loss: 0.9606 - val_accuracy: 0.3333\n",
      "Epoch 412/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.2516 - accuracy: 1.0000\n",
      "Epoch 412: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 412: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 37ms/step - loss: 0.2204 - accuracy: 1.0000 - val_loss: 0.9604 - val_accuracy: 0.3333\n",
      "Epoch 413/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.3785 - accuracy: 0.8750\n",
      "Epoch 413: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 413: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 49ms/step - loss: 0.3641 - accuracy: 0.8750 - val_loss: 0.9600 - val_accuracy: 0.3333\n",
      "Epoch 414/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.1727 - accuracy: 0.9375\n",
      "Epoch 414: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 414: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 55ms/step - loss: 0.2086 - accuracy: 0.9583 - val_loss: 0.9584 - val_accuracy: 0.3333\n",
      "Epoch 415/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.2593 - accuracy: 0.9375\n",
      "Epoch 415: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 415: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 43ms/step - loss: 0.2520 - accuracy: 0.9167 - val_loss: 0.9579 - val_accuracy: 0.3333\n",
      "Epoch 416/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.2664 - accuracy: 0.8125\n",
      "Epoch 416: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 416: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 42ms/step - loss: 0.3001 - accuracy: 0.8333 - val_loss: 0.9593 - val_accuracy: 0.3333\n",
      "Epoch 417/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.2221 - accuracy: 1.0000\n",
      "Epoch 417: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 417: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 28ms/step - loss: 0.2055 - accuracy: 1.0000 - val_loss: 0.9641 - val_accuracy: 0.3333\n",
      "Epoch 418/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.1871 - accuracy: 1.0000\n",
      "Epoch 418: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 418: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 50ms/step - loss: 0.1642 - accuracy: 1.0000 - val_loss: 0.9686 - val_accuracy: 0.3333\n",
      "Epoch 419/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.1875 - accuracy: 0.9375\n",
      "Epoch 419: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 419: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 31ms/step - loss: 0.1842 - accuracy: 0.9583 - val_loss: 0.9693 - val_accuracy: 0.3333\n",
      "Epoch 420/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.2670 - accuracy: 0.8750\n",
      "Epoch 420: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 420: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 29ms/step - loss: 0.2877 - accuracy: 0.8750 - val_loss: 0.9738 - val_accuracy: 0.3333\n",
      "Epoch 421/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.3420 - accuracy: 0.8750\n",
      "Epoch 421: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 421: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 30ms/step - loss: 0.3084 - accuracy: 0.9167 - val_loss: 0.9789 - val_accuracy: 0.3333\n",
      "Epoch 422/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.1670 - accuracy: 1.0000\n",
      "Epoch 422: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 422: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 38ms/step - loss: 0.2171 - accuracy: 0.9583 - val_loss: 0.9864 - val_accuracy: 0.3333\n",
      "Epoch 423/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.2861 - accuracy: 0.9375\n",
      "Epoch 423: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 423: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 30ms/step - loss: 0.2476 - accuracy: 0.9583 - val_loss: 0.9952 - val_accuracy: 0.3333\n",
      "Epoch 424/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.3056 - accuracy: 0.8750\n",
      "Epoch 424: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 424: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 0.3121 - accuracy: 0.9167 - val_loss: 0.9972 - val_accuracy: 0.3333\n",
      "Epoch 425/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.3099 - accuracy: 0.8750\n",
      "Epoch 425: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 425: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 0.2946 - accuracy: 0.8750 - val_loss: 1.0068 - val_accuracy: 0.3333\n",
      "Epoch 426/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.3593 - accuracy: 0.8750\n",
      "Epoch 426: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 426: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 29ms/step - loss: 0.4355 - accuracy: 0.8750 - val_loss: 1.0109 - val_accuracy: 0.3333\n",
      "Epoch 427/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.2704 - accuracy: 0.9375\n",
      "Epoch 427: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 427: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 43ms/step - loss: 0.2541 - accuracy: 0.9583 - val_loss: 1.0132 - val_accuracy: 0.1667\n",
      "Epoch 428/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.4603 - accuracy: 0.8125\n",
      "Epoch 428: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 428: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 28ms/step - loss: 0.3736 - accuracy: 0.8333 - val_loss: 1.0158 - val_accuracy: 0.1667\n",
      "Epoch 429/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.2655 - accuracy: 0.8750\n",
      "Epoch 429: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 429: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 28ms/step - loss: 0.2248 - accuracy: 0.9167 - val_loss: 1.0209 - val_accuracy: 0.1667\n",
      "Epoch 430/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.1809 - accuracy: 1.0000\n",
      "Epoch 430: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 430: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 0.1652 - accuracy: 1.0000 - val_loss: 1.0196 - val_accuracy: 0.1667\n",
      "Epoch 431/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.1538 - accuracy: 0.9375\n",
      "Epoch 431: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 431: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 28ms/step - loss: 0.2162 - accuracy: 0.9167 - val_loss: 1.0210 - val_accuracy: 0.1667\n",
      "Epoch 432/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.2103 - accuracy: 0.9375\n",
      "Epoch 432: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 432: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 31ms/step - loss: 0.1737 - accuracy: 0.9583 - val_loss: 1.0233 - val_accuracy: 0.1667\n",
      "Epoch 433/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.3903 - accuracy: 0.7500\n",
      "Epoch 433: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 433: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 53ms/step - loss: 0.4169 - accuracy: 0.7917 - val_loss: 1.0239 - val_accuracy: 0.1667\n",
      "Epoch 434/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.3624 - accuracy: 0.7500\n",
      "Epoch 434: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 434: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 28ms/step - loss: 0.3932 - accuracy: 0.7917 - val_loss: 1.0274 - val_accuracy: 0.1667\n",
      "Epoch 435/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.6249 - accuracy: 0.7500\n",
      "Epoch 435: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 435: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 0.4994 - accuracy: 0.7917 - val_loss: 1.0231 - val_accuracy: 0.1667\n",
      "Epoch 436/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.1666 - accuracy: 1.0000\n",
      "Epoch 436: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 436: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 30ms/step - loss: 0.2162 - accuracy: 0.9583 - val_loss: 1.0244 - val_accuracy: 0.1667\n",
      "Epoch 437/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.2290 - accuracy: 0.8750\n",
      "Epoch 437: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 437: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 0.2415 - accuracy: 0.8750 - val_loss: 1.0282 - val_accuracy: 0.1667\n",
      "Epoch 438/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.1858 - accuracy: 0.9375\n",
      "Epoch 438: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 438: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 30ms/step - loss: 0.1658 - accuracy: 0.9583 - val_loss: 1.0308 - val_accuracy: 0.1667\n",
      "Epoch 439/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.2708 - accuracy: 0.9375\n",
      "Epoch 439: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 439: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 0.2240 - accuracy: 0.9583 - val_loss: 1.0289 - val_accuracy: 0.1667\n",
      "Epoch 440/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.3455 - accuracy: 0.9375\n",
      "Epoch 440: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 440: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 40ms/step - loss: 0.3640 - accuracy: 0.9167 - val_loss: 1.0297 - val_accuracy: 0.3333\n",
      "Epoch 441/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.2502 - accuracy: 0.9375\n",
      "Epoch 441: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 441: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 28ms/step - loss: 0.4133 - accuracy: 0.9167 - val_loss: 1.0292 - val_accuracy: 0.3333\n",
      "Epoch 442/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.3194 - accuracy: 0.8750\n",
      "Epoch 442: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 442: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 29ms/step - loss: 0.2706 - accuracy: 0.9167 - val_loss: 1.0306 - val_accuracy: 0.3333\n",
      "Epoch 443/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.2607 - accuracy: 0.9375\n",
      "Epoch 443: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 443: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 0.2914 - accuracy: 0.9167 - val_loss: 1.0259 - val_accuracy: 0.3333\n",
      "Epoch 444/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.1959 - accuracy: 1.0000\n",
      "Epoch 444: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 444: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 0.2003 - accuracy: 1.0000 - val_loss: 1.0280 - val_accuracy: 0.3333\n",
      "Epoch 445/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.3960 - accuracy: 0.8750\n",
      "Epoch 445: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 445: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 0.3636 - accuracy: 0.8750 - val_loss: 1.0309 - val_accuracy: 0.3333\n",
      "Epoch 446/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.1751 - accuracy: 1.0000\n",
      "Epoch 446: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 446: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 56ms/step - loss: 0.2383 - accuracy: 0.9583 - val_loss: 1.0297 - val_accuracy: 0.3333\n",
      "Epoch 447/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.1674 - accuracy: 1.0000\n",
      "Epoch 447: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 447: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 31ms/step - loss: 0.2022 - accuracy: 0.9583 - val_loss: 1.0320 - val_accuracy: 0.3333\n",
      "Epoch 448/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.2053 - accuracy: 0.8750\n",
      "Epoch 448: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 448: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 32ms/step - loss: 0.2237 - accuracy: 0.9167 - val_loss: 1.0381 - val_accuracy: 0.3333\n",
      "Epoch 449/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.2355 - accuracy: 0.8750\n",
      "Epoch 449: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 449: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 31ms/step - loss: 0.3014 - accuracy: 0.8750 - val_loss: 1.0378 - val_accuracy: 0.3333\n",
      "Epoch 450/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.2640 - accuracy: 0.8750\n",
      "Epoch 450: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 450: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 0.2459 - accuracy: 0.9167 - val_loss: 1.0413 - val_accuracy: 0.3333\n",
      "Epoch 451/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.2368 - accuracy: 0.9375\n",
      "Epoch 451: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 451: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 0.1897 - accuracy: 0.9583 - val_loss: 1.0469 - val_accuracy: 0.1667\n",
      "Epoch 452/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.2682 - accuracy: 1.0000\n",
      "Epoch 452: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 452: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 0.2092 - accuracy: 1.0000 - val_loss: 1.0515 - val_accuracy: 0.1667\n",
      "Epoch 453/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.5600 - accuracy: 0.7500\n",
      "Epoch 453: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 453: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 56ms/step - loss: 0.4949 - accuracy: 0.7917 - val_loss: 1.0569 - val_accuracy: 0.1667\n",
      "Epoch 454/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.3718 - accuracy: 0.8750\n",
      "Epoch 454: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 454: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 0.3376 - accuracy: 0.8750 - val_loss: 1.0592 - val_accuracy: 0.1667\n",
      "Epoch 455/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.2781 - accuracy: 0.9375\n",
      "Epoch 455: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 455: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 30ms/step - loss: 0.2370 - accuracy: 0.9583 - val_loss: 1.0627 - val_accuracy: 0.1667\n",
      "Epoch 456/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.2001 - accuracy: 0.9375\n",
      "Epoch 456: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 456: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 0.2411 - accuracy: 0.9167 - val_loss: 1.0698 - val_accuracy: 0.1667\n",
      "Epoch 457/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.2180 - accuracy: 0.9375\n",
      "Epoch 457: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 457: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 29ms/step - loss: 0.1822 - accuracy: 0.9583 - val_loss: 1.0682 - val_accuracy: 0.1667\n",
      "Epoch 458/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.6290 - accuracy: 0.6875\n",
      "Epoch 458: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 458: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 0.5053 - accuracy: 0.7500 - val_loss: 1.0732 - val_accuracy: 0.1667\n",
      "Epoch 459/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.1447 - accuracy: 1.0000\n",
      "Epoch 459: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 459: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 30ms/step - loss: 0.1702 - accuracy: 0.9583 - val_loss: 1.0725 - val_accuracy: 0.1667\n",
      "Epoch 460/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.3684 - accuracy: 0.8750\n",
      "Epoch 460: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 460: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 91ms/step - loss: 0.3057 - accuracy: 0.9167 - val_loss: 1.0780 - val_accuracy: 0.1667\n",
      "Epoch 461/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.3765 - accuracy: 0.8750\n",
      "Epoch 461: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 461: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 28ms/step - loss: 0.3765 - accuracy: 0.8333 - val_loss: 1.0808 - val_accuracy: 0.1667\n",
      "Epoch 462/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.2590 - accuracy: 0.9375\n",
      "Epoch 462: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 462: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 0.2156 - accuracy: 0.9583 - val_loss: 1.0858 - val_accuracy: 0.1667\n",
      "Epoch 463/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.4298 - accuracy: 0.8750\n",
      "Epoch 463: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 463: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 29ms/step - loss: 0.3832 - accuracy: 0.9167 - val_loss: 1.0883 - val_accuracy: 0.1667\n",
      "Epoch 464/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.1423 - accuracy: 1.0000\n",
      "Epoch 464: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 464: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 0.2116 - accuracy: 0.9583 - val_loss: 1.0928 - val_accuracy: 0.1667\n",
      "Epoch 465/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.3535 - accuracy: 0.8125\n",
      "Epoch 465: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 465: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 0.3366 - accuracy: 0.8750 - val_loss: 1.0994 - val_accuracy: 0.1667\n",
      "Epoch 466/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.4603 - accuracy: 0.8750\n",
      "Epoch 466: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 466: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 0.3777 - accuracy: 0.8750 - val_loss: 1.1028 - val_accuracy: 0.1667\n",
      "Epoch 467/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.1622 - accuracy: 0.9375\n",
      "Epoch 467: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 467: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 30ms/step - loss: 0.1628 - accuracy: 0.9583 - val_loss: 1.1046 - val_accuracy: 0.1667\n",
      "Epoch 468/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.2515 - accuracy: 0.8750\n",
      "Epoch 468: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 468: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 28ms/step - loss: 0.2312 - accuracy: 0.8750 - val_loss: 1.1090 - val_accuracy: 0.1667\n",
      "Epoch 469/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.2399 - accuracy: 1.0000\n",
      "Epoch 469: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 469: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 0.2299 - accuracy: 0.9583 - val_loss: 1.1138 - val_accuracy: 0.1667\n",
      "Epoch 470/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.1979 - accuracy: 1.0000\n",
      "Epoch 470: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 470: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 0.2589 - accuracy: 0.9167 - val_loss: 1.1181 - val_accuracy: 0.1667\n",
      "Epoch 471/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.2895 - accuracy: 0.8750\n",
      "Epoch 471: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 471: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 38ms/step - loss: 0.2750 - accuracy: 0.9167 - val_loss: 1.1219 - val_accuracy: 0.1667\n",
      "Epoch 472/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.1489 - accuracy: 1.0000\n",
      "Epoch 472: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 472: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 29ms/step - loss: 0.1356 - accuracy: 1.0000 - val_loss: 1.1228 - val_accuracy: 0.1667\n",
      "Epoch 473/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.1877 - accuracy: 0.9375\n",
      "Epoch 473: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 473: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 0.2829 - accuracy: 0.9167 - val_loss: 1.1225 - val_accuracy: 0.1667\n",
      "Epoch 474/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.2825 - accuracy: 0.8750\n",
      "Epoch 474: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 474: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 28ms/step - loss: 0.3090 - accuracy: 0.8750 - val_loss: 1.1239 - val_accuracy: 0.1667\n",
      "Epoch 475/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.1379 - accuracy: 1.0000\n",
      "Epoch 475: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 475: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 0.1463 - accuracy: 0.9583 - val_loss: 1.1271 - val_accuracy: 0.1667\n",
      "Epoch 476/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.1351 - accuracy: 0.9375\n",
      "Epoch 476: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 476: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 30ms/step - loss: 0.1514 - accuracy: 0.9583 - val_loss: 1.1306 - val_accuracy: 0.1667\n",
      "Epoch 477/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.3090 - accuracy: 0.9375\n",
      "Epoch 477: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 477: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 30ms/step - loss: 0.2354 - accuracy: 0.9583 - val_loss: 1.1313 - val_accuracy: 0.1667\n",
      "Epoch 478/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.1382 - accuracy: 1.0000\n",
      "Epoch 478: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 478: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 0.1657 - accuracy: 1.0000 - val_loss: 1.1316 - val_accuracy: 0.1667\n",
      "Epoch 479/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.2124 - accuracy: 1.0000\n",
      "Epoch 479: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 479: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 0.2269 - accuracy: 1.0000 - val_loss: 1.1297 - val_accuracy: 0.1667\n",
      "Epoch 480/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.1421 - accuracy: 1.0000\n",
      "Epoch 480: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 480: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 0.2522 - accuracy: 0.9167 - val_loss: 1.1264 - val_accuracy: 0.1667\n",
      "Epoch 481/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.3566 - accuracy: 0.8125\n",
      "Epoch 481: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 481: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 28ms/step - loss: 0.3089 - accuracy: 0.8333 - val_loss: 1.1199 - val_accuracy: 0.1667\n",
      "Epoch 482/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.1542 - accuracy: 1.0000\n",
      "Epoch 482: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 482: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 41ms/step - loss: 0.1624 - accuracy: 1.0000 - val_loss: 1.1204 - val_accuracy: 0.1667\n",
      "Epoch 483/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.2277 - accuracy: 0.9375\n",
      "Epoch 483: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 483: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 28ms/step - loss: 0.2367 - accuracy: 0.9167 - val_loss: 1.1171 - val_accuracy: 0.1667\n",
      "Epoch 484/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.5771 - accuracy: 0.8125\n",
      "Epoch 484: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 484: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 45ms/step - loss: 0.5405 - accuracy: 0.7500 - val_loss: 1.1136 - val_accuracy: 0.1667\n",
      "Epoch 485/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.2956 - accuracy: 0.8750\n",
      "Epoch 485: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 485: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 28ms/step - loss: 0.3058 - accuracy: 0.8750 - val_loss: 1.1095 - val_accuracy: 0.1667\n",
      "Epoch 486/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.3997 - accuracy: 0.8750\n",
      "Epoch 486: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 486: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 0.3786 - accuracy: 0.8750 - val_loss: 1.1083 - val_accuracy: 0.1667\n",
      "Epoch 487/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.2501 - accuracy: 0.8750\n",
      "Epoch 487: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 487: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 37ms/step - loss: 0.3094 - accuracy: 0.8333 - val_loss: 1.1124 - val_accuracy: 0.1667\n",
      "Epoch 488/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.1894 - accuracy: 0.9375\n",
      "Epoch 488: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 488: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 28ms/step - loss: 0.2060 - accuracy: 0.9583 - val_loss: 1.1140 - val_accuracy: 0.1667\n",
      "Epoch 489/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.2948 - accuracy: 0.9375\n",
      "Epoch 489: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 489: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 0.3356 - accuracy: 0.9167 - val_loss: 1.1147 - val_accuracy: 0.1667\n",
      "Epoch 490/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.1918 - accuracy: 0.9375\n",
      "Epoch 490: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 490: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 0.1919 - accuracy: 0.9583 - val_loss: 1.1147 - val_accuracy: 0.1667\n",
      "Epoch 491/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.1876 - accuracy: 0.9375\n",
      "Epoch 491: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 491: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 30ms/step - loss: 0.2098 - accuracy: 0.9583 - val_loss: 1.1172 - val_accuracy: 0.1667\n",
      "Epoch 492/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.2438 - accuracy: 0.8750\n",
      "Epoch 492: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 492: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 51ms/step - loss: 0.2749 - accuracy: 0.8750 - val_loss: 1.1205 - val_accuracy: 0.1667\n",
      "Epoch 493/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.4299 - accuracy: 0.8750\n",
      "Epoch 493: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 493: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 0.4326 - accuracy: 0.8750 - val_loss: 1.1229 - val_accuracy: 0.1667\n",
      "Epoch 494/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.3187 - accuracy: 0.8750\n",
      "Epoch 494: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 494: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 28ms/step - loss: 0.2670 - accuracy: 0.9167 - val_loss: 1.1210 - val_accuracy: 0.1667\n",
      "Epoch 495/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.0996 - accuracy: 1.0000\n",
      "Epoch 495: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 495: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 32ms/step - loss: 0.1509 - accuracy: 0.9583 - val_loss: 1.1180 - val_accuracy: 0.1667\n",
      "Epoch 496/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.2773 - accuracy: 0.8750\n",
      "Epoch 496: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 496: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 0.4202 - accuracy: 0.8333 - val_loss: 1.1188 - val_accuracy: 0.1667\n",
      "Epoch 497/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.2903 - accuracy: 0.8750\n",
      "Epoch 497: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 497: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 29ms/step - loss: 0.2949 - accuracy: 0.8750 - val_loss: 1.1152 - val_accuracy: 0.1667\n",
      "Epoch 498/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.3114 - accuracy: 0.8750\n",
      "Epoch 498: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 498: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 42ms/step - loss: 0.2794 - accuracy: 0.8750 - val_loss: 1.1168 - val_accuracy: 0.1667\n",
      "Epoch 499/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.2727 - accuracy: 0.8750\n",
      "Epoch 499: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 499: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 0.2795 - accuracy: 0.8750 - val_loss: 1.1164 - val_accuracy: 0.1667\n",
      "Epoch 500/500\n",
      "1/2 [==============>...............] - ETA: 0s - loss: 0.2923 - accuracy: 0.9375\n",
      "Epoch 500: val_loss did not improve from 0.48488\n",
      "\n",
      "Epoch 500: val_accuracy did not improve from 0.66667\n",
      "2/2 [==============================] - 0s 33ms/step - loss: 0.2639 - accuracy: 0.9583 - val_loss: 1.1177 - val_accuracy: 0.1667\n"
     ]
    }
   ],
   "source": [
    "####################################################\n",
    "# FINE-TUNE BASE EEGNET MODEL ON NEW TRIAL SAMPLES\n",
    "####################################################\n",
    "\n",
    "# Configure\n",
    "retrain_model = False\n",
    "tensorboard_name = \"EEGNet_multisession_C0-C1-train_C2-test-samples-finetune_100hz\"\n",
    "best_finetune_model_filename = f\"./saved_variables/7/finetune-EEGNet/{tensorboard_name}\"\n",
    "\n",
    "# Get previously trained model\n",
    "keras_eegnet_model = TF_tools.load_lowest_loss_model(filepath= best_base_model_filename)\n",
    "\n",
    "# Unfreeze the base model\n",
    "keras_eegnet_model.trainable = True\n",
    "\n",
    "# Recompile base model\n",
    "keras_eegnet_model.compile(loss= 'categorical_crossentropy', \n",
    "                           optimizer= tf.keras.optimizers.Adam(learning_rate= 0.0001), \n",
    "                           metrics= [\"accuracy\"])\n",
    "\n",
    "# Retrain the base model\n",
    "## Note: the model is forced to use GPU, if GPU is not available replace with what is available e.g. /cpu:0\n",
    "## TODO: Change to CPU/GPU preference\n",
    "if (retrain_model): # Retrain or not\n",
    "    with tf.device('/cpu:0'):\n",
    "        keras_eegnet_model.fit(\n",
    "            x= mne_fixed_window_epochs_finetune_test_samples_data,\n",
    "            y= ohe_labels_finetune_test_samples,\n",
    "            batch_size= 16, # Default: 32 - 16 since we only have 30 samples\n",
    "            epochs= 500, # Default: 500 (EEGNet paper) - concur overfit\n",
    "            verbose= 1, # 0 = silent, 1 = progress bar, 2 = one line per epoch\n",
    "            callbacks= [TF_tools.tensorboard_callback(log_name= tensorboard_name),\n",
    "                        TF_tools.lowest_loss_model_save_callback(filepath= best_finetune_model_filename),\n",
    "                        TF_tools.highest_accuracy_model_save_callback(filepath= best_finetune_model_filename)],\n",
    "            validation_split= 0.2,\n",
    "            shuffle= True,\n",
    "            sample_weight= None, # Can be interesting due to time series\n",
    "            use_multiprocessing=True, # Done for faster speed\n",
    "            workers= 4 # Done for faster speed\n",
    "            )\n",
    "        \n",
    "# Delete unused variables\n",
    "del retrain_model\n",
    "del tensorboard_name\n",
    "del keras_eegnet_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "2fb52b5c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results for lowest loss model on unseen trial with finetuning\n",
      "Accuracy of: 0.6867599569429494\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Results for highest accuracy model on unseen trial with finetuning\n",
      "Accuracy of: 0.6178686759956943\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "####################################################\n",
    "# FINETUNED EEGNET MODEL PERFORMANCE\n",
    "####################################################\n",
    "\n",
    "# Convert labels back to original\n",
    "y_test = ohe.inverse_transform(ohe_labels_test)\n",
    "\n",
    "# Get results for best validation loss model\n",
    "print(\"Results for lowest loss model on unseen trial with finetuning\")\n",
    "keras_eegnet_model = TF_tools.load_lowest_loss_model(filepath= best_finetune_model_filename)\n",
    "\n",
    "y_pred = keras_eegnet_model.predict(mne_fixed_window_epochs_test_data)\n",
    "y_pred = ohe.inverse_transform(y_pred)\n",
    "\n",
    "accuracy =  accuracy_score(y_test, y_pred)\n",
    "\n",
    "print(f\"Accuracy of: {accuracy}\")\n",
    "ConfusionMatrixDisplay.from_predictions(y_true= y_test, y_pred= y_pred)\n",
    "plt.show()\n",
    "\n",
    "# Get results for best validation loss model\n",
    "print(\"\\n\\nResults for highest accuracy model on unseen trial with finetuning\")\n",
    "keras_eegnet_model = TF_tools.load_highest_accuracy_model(filepath= best_finetune_model_filename)\n",
    "\n",
    "y_pred = keras_eegnet_model.predict(mne_fixed_window_epochs_test_data)\n",
    "y_pred = ohe.inverse_transform(y_pred)\n",
    "\n",
    "accuracy =  accuracy_score(y_test, y_pred)\n",
    "\n",
    "print(f\"Accuracy of: {accuracy}\")\n",
    "ConfusionMatrixDisplay.from_predictions(y_true= y_test, y_pred= y_pred)\n",
    "plt.show()\n",
    "\n",
    "# Remove unused variables\n",
    "del accuracy\n",
    "del y_pred\n",
    "del y_test\n",
    "del keras_eegnet_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3358b6b7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "90725d27",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "008875bd",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bd23746d",
   "metadata": {},
   "outputs": [],
   "source": [
    "####################################################\n",
    "# CLEAUP\n",
    "####################################################\n",
    "\n",
    "# delete unused variables\n",
    "del best_model_filename\n",
    "del keras_eegnet_model\n",
    "\n",
    "del mne_fixed_window_epochs_test_data\n",
    "del mne_fixed_window_epochs_train_data\n",
    "del mne_fixed_window_epochs_finetune_test_samples\n",
    "\n",
    "del ohe\n",
    "\n",
    "del ohe_labels_finetune_test_samples\n",
    "del ohe_labels_test\n",
    "del ohe_labels_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fb820ee5",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "f92ed28e6a5fe026f22555c18fed88052bb861e5576fb72d2ac78e2247fef331"
  },
  "kernelspec": {
   "display_name": "Python 3.8.10 ('bci-master-thesis')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}