From 12ef02f4b87148364d77b37e30154f7c1bc839aa Mon Sep 17 00:00:00 2001
From: mahehsma <maxi.hehl@gmail.com>
Date: Sat, 29 Jun 2024 15:22:18 +0200
Subject: [PATCH] saving models; added first attempt for hyperparameter tuning

---
 Experiments.ipynb                             | 1509 +++++++++++++++--
 data/dataset_cleaned.csv                      |  298 ++++
 .../2024-06-29T15-18-20/fold0model.keras      |  Bin 0 -> 35304 bytes
 .../2024-06-29T15-18-20/fold1model.keras      |  Bin 0 -> 35304 bytes
 .../2024-06-29T15-18-20/fold2model.keras      |  Bin 0 -> 35304 bytes
 .../2024-06-29T15-18-20/fold3model.keras      |  Bin 0 -> 35304 bytes
 .../2024-06-29T15-18-20/fold4model.keras      |  Bin 0 -> 35304 bytes
 .../2024-06-29T15-18-20/fold5model.keras      |  Bin 0 -> 35304 bytes
 .../2024-06-29T15-18-20/fold6model.keras      |  Bin 0 -> 35304 bytes
 .../2024-06-29T15-18-20/fold7model.keras      |  Bin 0 -> 35304 bytes
 .../2024-06-29T15-18-20/fold8model.keras      |  Bin 0 -> 35304 bytes
 .../2024-06-29T15-18-20/fold9model.keras      |  Bin 0 -> 35304 bytes
 12 files changed, 1670 insertions(+), 137 deletions(-)
 create mode 100644 data/dataset_cleaned.csv
 create mode 100644 data/experiments/2024-06-29T15-18-20/fold0model.keras
 create mode 100644 data/experiments/2024-06-29T15-18-20/fold1model.keras
 create mode 100644 data/experiments/2024-06-29T15-18-20/fold2model.keras
 create mode 100644 data/experiments/2024-06-29T15-18-20/fold3model.keras
 create mode 100644 data/experiments/2024-06-29T15-18-20/fold4model.keras
 create mode 100644 data/experiments/2024-06-29T15-18-20/fold5model.keras
 create mode 100644 data/experiments/2024-06-29T15-18-20/fold6model.keras
 create mode 100644 data/experiments/2024-06-29T15-18-20/fold7model.keras
 create mode 100644 data/experiments/2024-06-29T15-18-20/fold8model.keras
 create mode 100644 data/experiments/2024-06-29T15-18-20/fold9model.keras

diff --git a/Experiments.ipynb b/Experiments.ipynb
index c7a8da7..504c353 100644
--- a/Experiments.ipynb
+++ b/Experiments.ipynb
@@ -253,7 +253,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 9,
    "id": "2bbee865-c000-43da-84d9-ce7e04874110",
    "metadata": {},
    "outputs": [],
@@ -272,7 +272,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 24,
    "id": "38eb4f87-ca3c-4ecf-a8ca-29422822d933",
    "metadata": {},
    "outputs": [
@@ -280,45 +280,48 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "saving models under \"data/experiments/2024-06-29T15-18-20/\"\n",
       "Training 10 folds for 20 epochs\n",
       "Fold 0\n",
       "\tTrain samples:\t267\tTest samples:\t30\n",
-      "\tAccuracy: 90.000%\n",
+      "\tAccuracy: 86.667%\n",
       "Fold 1\n",
       "\tTrain samples:\t267\tTest samples:\t30\n",
-      "\tAccuracy: 86.667%\n",
+      "\tAccuracy: 80.000%\n",
       "Fold 2\n",
       "\tTrain samples:\t267\tTest samples:\t30\n",
-      "\tAccuracy: 90.000%\n",
+      "\tAccuracy: 86.667%\n",
       "Fold 3\n",
       "\tTrain samples:\t267\tTest samples:\t30\n",
-      "\tAccuracy: 93.333%\n",
+      "\tAccuracy: 90.000%\n",
       "Fold 4\n",
       "\tTrain samples:\t267\tTest samples:\t30\n",
-      "WARNING:tensorflow:5 out of the last 5 calls to <function TensorFlowTrainer.make_predict_function.<locals>.one_step_on_data_distributed at 0x0000024C840482C0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
-      "\tAccuracy: 83.333%\n",
+      "\tAccuracy: 90.000%\n",
       "Fold 5\n",
       "\tTrain samples:\t267\tTest samples:\t30\n",
-      "WARNING:tensorflow:6 out of the last 6 calls to <function TensorFlowTrainer.make_predict_function.<locals>.one_step_on_data_distributed at 0x0000024C867CF920> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
-      "\tAccuracy: 90.000%\n",
+      "\tAccuracy: 86.667%\n",
       "Fold 6\n",
       "\tTrain samples:\t267\tTest samples:\t30\n",
-      "\tAccuracy: 76.667%\n",
+      "\tAccuracy: 90.000%\n",
       "Fold 7\n",
       "\tTrain samples:\t268\tTest samples:\t29\n",
-      "\tAccuracy: 89.655%\n",
+      "\tAccuracy: 82.759%\n",
       "Fold 8\n",
       "\tTrain samples:\t268\tTest samples:\t29\n",
-      "\tAccuracy: 79.310%\n",
+      "\tAccuracy: 75.862%\n",
       "Fold 9\n",
       "\tTrain samples:\t268\tTest samples:\t29\n",
       "\tAccuracy: 79.310%\n",
-      "Avg accuracy 85.828%\n"
+      "Avg accuracy 84.793%\n"
      ]
     }
    ],
    "source": [
     "import tensorflow as tf\n",
+    "import datetime as dt\n",
+    "import os\n",
+    "\n",
+    "save_model = True\n",
     "\n",
     "use_pca = True\n",
     "# number of components extracted from the pca\n",
@@ -331,6 +334,12 @@
     "# used to split the dataset into k folds\n",
     "kf = KFold(n_splits=k_folds)\n",
     "\n",
+    "if save_model:\n",
+    "    timestamp = dt.datetime.now().strftime('%Y-%m-%dT%H-%M-%S')\n",
+    "    base_path = f'data/experiments/{timestamp}/'\n",
+    "    print(f'saving models under \"{base_path}\"')\n",
+    "    os.makedirs(base_path)\n",
+    "\n",
     "accuracies = []\n",
     "print(f'Training {k_folds} folds for {epochs} epochs')\n",
     "for i, (train_idx, test_idx) in enumerate(kf.split(X)):\n",
@@ -353,6 +362,9 @@
     "    model = get_model(n_features)\n",
     "    model.fit(X_train, y_train, epochs=epochs, verbose=0)\n",
     "\n",
+    "    if save_model:\n",
+    "        model.save(base_path + f'fold{i}model.keras')\n",
+    "\n",
     "    if use_pca:\n",
     "        # transform test data using on the pca model trained on the train data\n",
     "        X_test = pca.transform(X_test)\n",
@@ -370,6 +382,40 @@
     "print(f'Avg accuracy {avg_accuracy:.3%}')"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "241cc0c7-f638-4481-afd3-e0f9d5e0dd59",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
+      "Patient 1 \n",
+      "prediction:\thealthy \n",
+      "ground truth:\thealthy\n"
+     ]
+    }
+   ],
+   "source": [
+    "index = 0\n",
+    "\n",
+    "patient = X.iloc[[index]]\n",
+    "ground_truth = y[index]\n",
+    "\n",
+    "x = pca.transform(patient)\n",
+    "\n",
+    "prediction = model.predict([x])\n",
+    "def get_health_status(val): \n",
+    "    return 'healthy' if val < 0.5 else 'sick'\n",
+    "    \n",
+    "print(f'''Patient {index + 1} \n",
+    "prediction:\\t{get_health_status(prediction[0,0])} \n",
+    "ground truth:\\t{get_health_status(ground_truth[0])}''')"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 5,
@@ -382,93 +428,23 @@
      "text": [
       "Training 5 folds\n",
       "Fold 0\n",
-      "\tTrain samples:\t237\tTest samples:\t60\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\maxwi\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "\tTrain samples:\t237\tTest samples:\t60\n",
       "\tAccuracy 58.333%\n",
       "\n",
       "Fold 1\n",
-      "\tTrain samples:\t237\tTest samples:\t60\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\maxwi\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "\tTrain samples:\t237\tTest samples:\t60\n",
       "\tAccuracy 50.000%\n",
       "\n",
       "Fold 2\n",
-      "\tTrain samples:\t238\tTest samples:\t59\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\maxwi\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "\tTrain samples:\t238\tTest samples:\t59\n",
       "\tAccuracy 55.932%\n",
       "\n",
       "Fold 3\n",
-      "\tTrain samples:\t238\tTest samples:\t59\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\maxwi\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "\tTrain samples:\t238\tTest samples:\t59\n",
       "\tAccuracy 57.627%\n",
       "\n",
       "Fold 4\n",
-      "\tTrain samples:\t238\tTest samples:\t59\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\maxwi\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "\tTrain samples:\t238\tTest samples:\t59\n",
       "\tAccuracy 52.542%\n",
       "\n",
       "Avg accuracy 54.887%\n"
@@ -543,21 +519,21 @@
       "\n",
       "Fold 1\n",
       "\tTrain samples:\t237\tTest samples:\t60\n",
-      "\tAccuracy 90.000%\n",
+      "\tAccuracy 91.667%\n",
       "\n",
       "Fold 2\n",
       "\tTrain samples:\t238\tTest samples:\t59\n",
-      "\tAccuracy 84.746%\n",
+      "\tAccuracy 79.661%\n",
       "\n",
       "Fold 3\n",
       "\tTrain samples:\t238\tTest samples:\t59\n",
-      "\tAccuracy 76.271%\n",
+      "\tAccuracy 79.661%\n",
       "\n",
       "Fold 4\n",
       "\tTrain samples:\t238\tTest samples:\t59\n",
       "\tAccuracy 77.966%\n",
       "\n",
-      "Avg accuracy 82.797%\n"
+      "Avg accuracy 82.791%\n"
      ]
     }
    ],
@@ -625,21 +601,417 @@
    "id": "79631688-07cb-450d-9958-8d8341722d7d",
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\maxwi\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=2.\n",
-      "  warnings.warn(\n"
-     ]
-    },
     {
      "data": {
       "text/html": [
-       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KMeans(n_clusters=2, n_init=&#x27;auto&#x27;, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KMeans</label><div class=\"sk-toggleable__content\"><pre>KMeans(n_clusters=2, n_init=&#x27;auto&#x27;, random_state=42)</pre></div></div></div></div></div>"
+       "<style>#sk-container-id-1 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-1 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-1 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-1 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KMeans(n_clusters=2, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;KMeans<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.cluster.KMeans.html\">?<span>Documentation for KMeans</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>KMeans(n_clusters=2, random_state=42)</pre></div> </div></div></div></div>"
       ],
       "text/plain": [
-       "KMeans(n_clusters=2, n_init='auto', random_state=42)"
+       "KMeans(n_clusters=2, random_state=42)"
       ]
      },
      "execution_count": 7,
@@ -712,13 +1084,13 @@
       " 4.17139647e-02 3.17012077e-02 2.52492654e-02 2.21354486e-02\n",
       " 1.84895571e-02 1.74748048e-02 8.28895271e-03 5.47222590e-03\n",
       " 4.87868838e-03 3.91078109e-03 3.44014667e-03 2.69161359e-03\n",
-      " 5.88469272e-33 3.26180402e-33 1.59388562e-33 1.39694325e-33\n",
-      " 1.30446173e-33 1.30446173e-33 1.30446173e-33 1.11776656e-34]\n"
+      " 7.10960871e-18 6.62254449e-18 0.00000000e+00 0.00000000e+00\n",
+      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00]\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 800x600 with 1 Axes>"
       ]
@@ -767,34 +1139,34 @@
      "text": [
       "Contributions of features to the first principal component:\n",
       "      Feature  Contribution\n",
-      "17    exang_1      0.332492\n",
-      "23   thal_7.0      0.331968\n",
-      "10       cp_4      0.328138\n",
-      "19    slope_2      0.272838\n",
-      "6       sex_1      0.196119\n",
-      "15  restecg_2      0.131840\n",
-      "25     ca_1.0      0.107372\n",
-      "4     oldpeak      0.094386\n",
-      "26     ca_2.0      0.078705\n",
-      "0         age      0.046441\n",
-      "27     ca_3.0      0.046405\n",
-      "22   thal_6.0      0.041738\n",
-      "1    trestbps      0.020995\n",
-      "20    slope_3      0.020165\n",
-      "12      fbs_1      0.013534\n",
-      "2        chol      0.005950\n",
-      "14  restecg_1      0.004777\n",
-      "7        cp_1     -0.009925\n",
-      "11      fbs_0     -0.013534\n",
-      "3     thalach     -0.092913\n",
-      "13  restecg_0     -0.136618\n",
-      "8        cp_2     -0.139917\n",
-      "9        cp_3     -0.178297\n",
-      "5       sex_0     -0.196119\n",
-      "24     ca_0.0     -0.232482\n",
-      "18    slope_1     -0.293003\n",
-      "16    exang_0     -0.332492\n",
-      "21   thal_3.0     -0.373706\n"
+      "21   thal_3.0      0.373706\n",
+      "16    exang_0      0.332492\n",
+      "18    slope_1      0.293003\n",
+      "24     ca_0.0      0.232482\n",
+      "5       sex_0      0.196119\n",
+      "9        cp_3      0.178297\n",
+      "8        cp_2      0.139917\n",
+      "13  restecg_0      0.136618\n",
+      "3     thalach      0.092913\n",
+      "11      fbs_0      0.013534\n",
+      "7        cp_1      0.009925\n",
+      "14  restecg_1     -0.004777\n",
+      "2        chol     -0.005950\n",
+      "12      fbs_1     -0.013534\n",
+      "20    slope_3     -0.020165\n",
+      "1    trestbps     -0.020995\n",
+      "22   thal_6.0     -0.041738\n",
+      "27     ca_3.0     -0.046405\n",
+      "0         age     -0.046441\n",
+      "26     ca_2.0     -0.078705\n",
+      "4     oldpeak     -0.094386\n",
+      "25     ca_1.0     -0.107372\n",
+      "15  restecg_2     -0.131840\n",
+      "6       sex_1     -0.196119\n",
+      "19    slope_2     -0.272838\n",
+      "10       cp_4     -0.328138\n",
+      "23   thal_7.0     -0.331968\n",
+      "17    exang_1     -0.332492\n"
      ]
     }
    ],
@@ -822,13 +1194,876 @@
     "\n",
     "Hier würde das bedeuten, dass 'exang_1' (existing exercised induced angina), 'thal_7' (reversable effect caused by thalassemia) und cp_4 (asymptomatic type of chest pain) einen potenziell größeren Einfluss auf die Zielvariable haben, als andere Merkmale."
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "6ebdafbb-302b-4fbf-822a-f621419db8ec",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.8295  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6515  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7714  \n",
+      "WARNING:tensorflow:5 out of the last 5 calls to <function TensorFlowTrainer.make_predict_function.<locals>.one_step_on_data_distributed at 0x000001788601F880> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
+      "\u001b[1m1/2\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 38ms/stepWARNING:tensorflow:6 out of the last 6 calls to <function TensorFlowTrainer.make_predict_function.<locals>.one_step_on_data_distributed at 0x000001788601F880> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 854us/step - loss: 0.7180\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 851us/step - loss: 0.7831\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6749  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 923us/step - loss: 0.7142\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 909us/step - loss: 0.6590\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 992us/step - loss: 0.6109\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 979us/step - loss: 0.7255\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 926us/step - loss: 0.6575\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 990us/step - loss: 0.6600\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 922us/step - loss: 0.6714\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 989us/step - loss: 0.7193\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 921us/step - loss: 0.7402\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7113  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 992us/step - loss: 0.7141\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 921us/step - loss: 0.7562\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7777  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 780us/step - loss: 0.6970\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6871  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7026  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 994us/step - loss: 0.7408\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6920  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.8896  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6989  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 921us/step - loss: 0.7702\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7352  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 996us/step - loss: 0.7197\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 996us/step - loss: 0.7048\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 987us/step - loss: 0.7161\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 922us/step - loss: 0.7558\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7031  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 922us/step - loss: 0.6900\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 922us/step - loss: 0.6978\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 922us/step - loss: 0.6753\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6918  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 926us/step - loss: 0.7076\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 992us/step - loss: 0.6576\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7001  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6824  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7140  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7214  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6517  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6740  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 992us/step - loss: 0.6869\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7061  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6885\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7253  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 921us/step - loss: 0.7160\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6791  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7484  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 996us/step - loss: 0.6670\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6658  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6987  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6645  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6862  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6857  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 996us/step - loss: 0.6683\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6789  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7941  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 850us/step - loss: 0.7271\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6558  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 851us/step - loss: 0.6191\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6739  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7143  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 996us/step - loss: 0.7528\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 921us/step - loss: 0.6915\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 992us/step - loss: 0.8361\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 992us/step - loss: 0.7930\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 996us/step - loss: 0.7427\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.9099  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7453  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 853us/step - loss: 0.7005\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.9573  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 851us/step - loss: 0.7130\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.8095  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.8389  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 921us/step - loss: 0.6892\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 996us/step - loss: 0.6874\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7163  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 996us/step - loss: 0.8445\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6903  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7781  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7024  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 992us/step - loss: 0.7967\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 992us/step - loss: 0.7862\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7345  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6840  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7604  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 995us/step - loss: 0.7287\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7054  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 992us/step - loss: 0.8665\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 996us/step - loss: 0.7714\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 922us/step - loss: 0.7492\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 996us/step - loss: 0.7961\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 921us/step - loss: 0.6954\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 996us/step - loss: 0.6938\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7146  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 921us/step - loss: 0.7200\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 922us/step - loss: 0.6957\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 932us/step - loss: 0.7019\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7722  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 926us/step - loss: 0.6975\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 921us/step - loss: 0.6949\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 996us/step - loss: 0.7357\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 992us/step - loss: 0.7320\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 992us/step - loss: 0.7740\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 997us/step - loss: 0.7238\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 993us/step - loss: 0.7382\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7942  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6951  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 992us/step - loss: 0.8289\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 993us/step - loss: 0.7189\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7031  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6978  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6935  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 994us/step - loss: 0.7488\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.8922  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 992us/step - loss: 0.7063\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.8737  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 923us/step - loss: 0.7398\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 922us/step - loss: 0.6154\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 779us/step - loss: 0.6411\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.8713  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6880  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 851us/step - loss: 0.6130\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7781  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 992us/step - loss: 0.6757\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 923us/step - loss: 0.7560\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7059  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.8223  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 850us/step - loss: 0.7053\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6665  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 990us/step - loss: 0.6949\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 996us/step - loss: 0.6004\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 993us/step - loss: 0.8062\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 925us/step - loss: 0.6948\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 851us/step - loss: 0.7256\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 992us/step - loss: 0.7181\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 1.1155  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 925us/step - loss: 0.7132\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.5416  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 998us/step - loss: 0.6809\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 996us/step - loss: 0.8316\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7178  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.5664  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7293  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6615  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 854us/step - loss: 0.6773\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7038  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 922us/step - loss: 0.7680\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6712  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7196  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 994us/step - loss: 0.9046\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.5976  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7099  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7293  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 989us/step - loss: 0.5528\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 921us/step - loss: 0.5784\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 921us/step - loss: 0.6347\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7937  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6946  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6404  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 996us/step - loss: 0.8364\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 992us/step - loss: 0.6745\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7491  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7244  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 1ms/step - loss: 0.7381\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 922us/step - loss: 0.8197\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 925us/step - loss: 0.5966\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6577  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6440  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.9688  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 996us/step - loss: 0.6320\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 997us/step - loss: 0.6709\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 993us/step - loss: 0.5891\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6918  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6092  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.5292  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 990us/step - loss: 0.6578\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 851us/step - loss: 0.6409\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 782us/step - loss: 0.6539\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 992us/step - loss: 0.7479\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 946us/step - loss: 0.6264\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 854us/step - loss: 0.7325\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 996us/step - loss: 0.7073\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 992us/step - loss: 0.7077\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 924us/step - loss: 0.7472\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 922us/step - loss: 0.7632\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6719  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7138  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 992us/step - loss: 0.6891\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 922us/step - loss: 0.6417\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 921us/step - loss: 0.7360\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 854us/step - loss: 0.6282\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 994us/step - loss: 0.7520\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 991us/step - loss: 0.6770\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 925us/step - loss: 0.7487\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7311  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 993us/step - loss: 0.7529\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 922us/step - loss: 0.6746\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 922us/step - loss: 0.6126\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7123  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7400  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6781  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 851us/step - loss: 0.6496\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7080  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 23ms/step \n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 922us/step - loss: 0.6861\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6657  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6910  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 851us/step - loss: 0.6611\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6790  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 997us/step - loss: 0.6587\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 994us/step - loss: 0.7418\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 990us/step - loss: 0.7174\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 922us/step - loss: 0.7292\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7172  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 992us/step - loss: 0.7383\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6175  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7076  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7297  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7053  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7079  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7391  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 922us/step - loss: 0.7462\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 928us/step - loss: 0.7051\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6867  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 996us/step - loss: 0.7039\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6947  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6746  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7012  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 927us/step - loss: 0.6957\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6848  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 994us/step - loss: 0.6887\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 996us/step - loss: 0.6940\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 997us/step - loss: 0.6847\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 992us/step - loss: 0.6910\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6544  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7138  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 851us/step - loss: 0.7806\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 921us/step - loss: 0.7403\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 921us/step - loss: 0.7008\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 993us/step - loss: 0.6308\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 922us/step - loss: 0.7127\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 925us/step - loss: 0.7144\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 937us/step - loss: 0.6850\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 925us/step - loss: 0.6720\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 851us/step - loss: 0.6723\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 921us/step - loss: 0.6892\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 851us/step - loss: 0.7354\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 992us/step - loss: 0.7456\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 921us/step - loss: 0.7177\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 922us/step - loss: 0.6997\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 996us/step - loss: 0.8523\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 851us/step - loss: 0.7809\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 995us/step - loss: 0.7619\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6454  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 992us/step - loss: 0.6675\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 994us/step - loss: 0.7363\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6864  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 921us/step - loss: 0.7198\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7432  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7373  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7160  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 925us/step - loss: 0.6948\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 921us/step - loss: 0.7250\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6951  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 993us/step - loss: 0.7817\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7065  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 922us/step - loss: 0.7340\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 927us/step - loss: 0.6945\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7972  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 926us/step - loss: 0.7228\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6888  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 996us/step - loss: 0.6892\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7002  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 921us/step - loss: 0.6750\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7213  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 921us/step - loss: 0.7113\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 992us/step - loss: 1.0239\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.8441  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7696  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6923  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.8282  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7153  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7243  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7386  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 100ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 992us/step - loss: 0.7519\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 926us/step - loss: 0.7155\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 922us/step - loss: 0.6842\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7463  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 1.1698  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7521  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7376  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7045  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.8476  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 996us/step - loss: 0.6859\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6958  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 1ms/step - loss: 0.6966\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 851us/step - loss: 1.0262\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 926us/step - loss: 0.8271\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 850us/step - loss: 0.6157\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 992us/step - loss: 0.8806\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 851us/step - loss: 0.8957\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 780us/step - loss: 0.8391\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 921us/step - loss: 0.7207\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 992us/step - loss: 0.6150\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 922us/step - loss: 0.7824\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6666  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7245  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 996us/step - loss: 0.6898\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 994us/step - loss: 0.7546\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.9641  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 921us/step - loss: 0.6203\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 852us/step - loss: 0.6156\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 922us/step - loss: 0.6758\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 921us/step - loss: 0.8885\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 992us/step - loss: 0.7606\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 992us/step - loss: 0.6680\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.8330  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 874us/step - loss: 0.8508\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 992us/step - loss: 0.7399\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 921us/step - loss: 0.8580\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6426  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 850us/step - loss: 0.6140\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 992us/step - loss: 0.7189\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 992us/step - loss: 0.6730\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 992us/step - loss: 0.8858\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 922us/step - loss: 0.7204\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 999us/step - loss: 0.7309\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 926us/step - loss: 0.9185\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.8882  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 925us/step - loss: 0.6012\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 992us/step - loss: 0.6738\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.5359  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6653  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7506  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 999us/step - loss: 0.6293\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7118  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 922us/step - loss: 0.6357\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7046  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7358  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 996us/step - loss: 0.8979\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6445  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.7247  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.5572  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6410  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 993us/step - loss: 0.9214\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 997us/step - loss: 0.6568\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 992us/step - loss: 0.6227\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6472  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 992us/step - loss: 0.6863\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6029  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.5962  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.8621  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6840  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6159  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6125  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.5928  \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6780  \n"
+     ]
+    }
+   ],
+   "source": [
+    "# hyperparameter tuning\n",
+    "from scikeras.wrappers import KerasClassifier\n",
+    "from sklearn.model_selection import GridSearchCV\n",
+    "\n",
+    "param_grid = [\n",
+    "    dict(hidden_layers= [1, 2, 3], dropout=[True, False], hidden_neurons= [10, 20, 30, 40], hidden_activation= ['relu', 'sigmoid', 'tanh'])\n",
+    "]\n",
+    "\n",
+    "def create_model(input_size=13, hidden_layers=2, dropout=False, hidden_neurons=10, hidden_activation='relu'):\n",
+    "    model = tf.keras.models.Sequential([\n",
+    "        tf.keras.layers.InputLayer(shape=(input_size,), name='input')\n",
+    "    ])\n",
+    "\n",
+    "    for i in range(hidden_layers):\n",
+    "        model.add(tf.keras.layers.Dense(hidden_neurons, activation=hidden_activation, name=f'hidden{i}'))\n",
+    "                                    \n",
+    "    model.add(tf.keras.layers.Dense(1, activation='sigmoid', name='output'))\n",
+    "    model.compile(optimizer=tf.keras.optimizers.Adam(), \n",
+    "                  loss=tf.keras.losses.BinaryCrossentropy())\n",
+    "    return model\n",
+    "\n",
+    "model = KerasClassifier(model=create_model, input_size=8, hidden_layers=2, dropout=False, hidden_neurons=10, hidden_activation='relu')\n",
+    "grid = GridSearchCV(estimator=model, param_grid=param_grid, cv=5)\n",
+    "\n",
+    "pca = decomposition.PCA(n_components=8)\n",
+    "pca.fit(X)\n",
+    "X_train = pca.transform(X)\n",
+    "grid_result = grid.fit(X_train, y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "80fc59e7-b9e4-40fd-84cb-ebb0c79411c2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Best: 0.797910 using {'dropout': True, 'hidden_activation': 'tanh', 'hidden_layers': 3, 'hidden_neurons': 40}\n",
+      "0.454746 (0.161661) with: {'dropout': True, 'hidden_activation': 'relu', 'hidden_layers': 1, 'hidden_neurons': 10}\n",
+      "0.579379 (0.133969) with: {'dropout': True, 'hidden_activation': 'relu', 'hidden_layers': 1, 'hidden_neurons': 20}\n",
+      "0.531808 (0.109664) with: {'dropout': True, 'hidden_activation': 'relu', 'hidden_layers': 1, 'hidden_neurons': 30}\n",
+      "0.457797 (0.088543) with: {'dropout': True, 'hidden_activation': 'relu', 'hidden_layers': 1, 'hidden_neurons': 40}\n",
+      "0.525424 (0.063975) with: {'dropout': True, 'hidden_activation': 'relu', 'hidden_layers': 2, 'hidden_neurons': 10}\n",
+      "0.539153 (0.094907) with: {'dropout': True, 'hidden_activation': 'relu', 'hidden_layers': 2, 'hidden_neurons': 20}\n",
+      "0.529040 (0.104087) with: {'dropout': True, 'hidden_activation': 'relu', 'hidden_layers': 2, 'hidden_neurons': 30}\n",
+      "0.676667 (0.042718) with: {'dropout': True, 'hidden_activation': 'relu', 'hidden_layers': 2, 'hidden_neurons': 40}\n",
+      "0.569266 (0.071501) with: {'dropout': True, 'hidden_activation': 'relu', 'hidden_layers': 3, 'hidden_neurons': 10}\n",
+      "0.559209 (0.036824) with: {'dropout': True, 'hidden_activation': 'relu', 'hidden_layers': 3, 'hidden_neurons': 20}\n",
+      "0.613277 (0.080455) with: {'dropout': True, 'hidden_activation': 'relu', 'hidden_layers': 3, 'hidden_neurons': 30}\n",
+      "0.763898 (0.058879) with: {'dropout': True, 'hidden_activation': 'relu', 'hidden_layers': 3, 'hidden_neurons': 40}\n",
+      "0.610056 (0.104976) with: {'dropout': True, 'hidden_activation': 'sigmoid', 'hidden_layers': 1, 'hidden_neurons': 10}\n",
+      "0.451582 (0.071647) with: {'dropout': True, 'hidden_activation': 'sigmoid', 'hidden_layers': 1, 'hidden_neurons': 20}\n",
+      "0.491751 (0.042408) with: {'dropout': True, 'hidden_activation': 'sigmoid', 'hidden_layers': 1, 'hidden_neurons': 30}\n",
+      "0.532203 (0.072876) with: {'dropout': True, 'hidden_activation': 'sigmoid', 'hidden_layers': 1, 'hidden_neurons': 40}\n",
+      "0.478192 (0.032344) with: {'dropout': True, 'hidden_activation': 'sigmoid', 'hidden_layers': 2, 'hidden_neurons': 10}\n",
+      "0.504859 (0.038705) with: {'dropout': True, 'hidden_activation': 'sigmoid', 'hidden_layers': 2, 'hidden_neurons': 20}\n",
+      "0.474576 (0.029586) with: {'dropout': True, 'hidden_activation': 'sigmoid', 'hidden_layers': 2, 'hidden_neurons': 30}\n",
+      "0.508475 (0.038078) with: {'dropout': True, 'hidden_activation': 'sigmoid', 'hidden_layers': 2, 'hidden_neurons': 40}\n",
+      "0.521808 (0.032344) with: {'dropout': True, 'hidden_activation': 'sigmoid', 'hidden_layers': 3, 'hidden_neurons': 10}\n",
+      "0.504859 (0.038705) with: {'dropout': True, 'hidden_activation': 'sigmoid', 'hidden_layers': 3, 'hidden_neurons': 20}\n",
+      "0.521808 (0.032344) with: {'dropout': True, 'hidden_activation': 'sigmoid', 'hidden_layers': 3, 'hidden_neurons': 30}\n",
+      "0.538757 (0.004428) with: {'dropout': True, 'hidden_activation': 'sigmoid', 'hidden_layers': 3, 'hidden_neurons': 40}\n",
+      "0.478588 (0.096403) with: {'dropout': True, 'hidden_activation': 'tanh', 'hidden_layers': 1, 'hidden_neurons': 10}\n",
+      "0.554915 (0.115371) with: {'dropout': True, 'hidden_activation': 'tanh', 'hidden_layers': 1, 'hidden_neurons': 20}\n",
+      "0.596328 (0.152314) with: {'dropout': True, 'hidden_activation': 'tanh', 'hidden_layers': 1, 'hidden_neurons': 30}\n",
+      "0.582655 (0.123923) with: {'dropout': True, 'hidden_activation': 'tanh', 'hidden_layers': 1, 'hidden_neurons': 40}\n",
+      "0.542316 (0.151461) with: {'dropout': True, 'hidden_activation': 'tanh', 'hidden_layers': 2, 'hidden_neurons': 10}\n",
+      "0.642655 (0.073907) with: {'dropout': True, 'hidden_activation': 'tanh', 'hidden_layers': 2, 'hidden_neurons': 20}\n",
+      "0.649774 (0.075395) with: {'dropout': True, 'hidden_activation': 'tanh', 'hidden_layers': 2, 'hidden_neurons': 30}\n",
+      "0.737119 (0.071569) with: {'dropout': True, 'hidden_activation': 'tanh', 'hidden_layers': 2, 'hidden_neurons': 40}\n",
+      "0.592260 (0.173314) with: {'dropout': True, 'hidden_activation': 'tanh', 'hidden_layers': 3, 'hidden_neurons': 10}\n",
+      "0.585085 (0.145203) with: {'dropout': True, 'hidden_activation': 'tanh', 'hidden_layers': 3, 'hidden_neurons': 20}\n",
+      "0.689548 (0.185362) with: {'dropout': True, 'hidden_activation': 'tanh', 'hidden_layers': 3, 'hidden_neurons': 30}\n",
+      "0.797910 (0.045237) with: {'dropout': True, 'hidden_activation': 'tanh', 'hidden_layers': 3, 'hidden_neurons': 40}\n",
+      "0.612486 (0.088991) with: {'dropout': False, 'hidden_activation': 'relu', 'hidden_layers': 1, 'hidden_neurons': 10}\n",
+      "0.535650 (0.071748) with: {'dropout': False, 'hidden_activation': 'relu', 'hidden_layers': 1, 'hidden_neurons': 20}\n",
+      "0.581921 (0.084089) with: {'dropout': False, 'hidden_activation': 'relu', 'hidden_layers': 1, 'hidden_neurons': 30}\n",
+      "0.589096 (0.117795) with: {'dropout': False, 'hidden_activation': 'relu', 'hidden_layers': 1, 'hidden_neurons': 40}\n",
+      "0.481921 (0.147599) with: {'dropout': False, 'hidden_activation': 'relu', 'hidden_layers': 2, 'hidden_neurons': 10}\n",
+      "0.639492 (0.082682) with: {'dropout': False, 'hidden_activation': 'relu', 'hidden_layers': 2, 'hidden_neurons': 20}\n",
+      "0.612203 (0.097082) with: {'dropout': False, 'hidden_activation': 'relu', 'hidden_layers': 2, 'hidden_neurons': 30}\n",
+      "0.542599 (0.096070) with: {'dropout': False, 'hidden_activation': 'relu', 'hidden_layers': 2, 'hidden_neurons': 40}\n",
+      "0.428418 (0.120285) with: {'dropout': False, 'hidden_activation': 'relu', 'hidden_layers': 3, 'hidden_neurons': 10}\n",
+      "0.528588 (0.067879) with: {'dropout': False, 'hidden_activation': 'relu', 'hidden_layers': 3, 'hidden_neurons': 20}\n",
+      "0.650169 (0.093519) with: {'dropout': False, 'hidden_activation': 'relu', 'hidden_layers': 3, 'hidden_neurons': 30}\n",
+      "0.663785 (0.068932) with: {'dropout': False, 'hidden_activation': 'relu', 'hidden_layers': 3, 'hidden_neurons': 40}\n",
+      "0.562825 (0.123017) with: {'dropout': False, 'hidden_activation': 'sigmoid', 'hidden_layers': 1, 'hidden_neurons': 10}\n",
+      "0.501751 (0.047258) with: {'dropout': False, 'hidden_activation': 'sigmoid', 'hidden_layers': 1, 'hidden_neurons': 20}\n",
+      "0.434915 (0.103495) with: {'dropout': False, 'hidden_activation': 'sigmoid', 'hidden_layers': 1, 'hidden_neurons': 30}\n",
+      "0.583051 (0.122324) with: {'dropout': False, 'hidden_activation': 'sigmoid', 'hidden_layers': 1, 'hidden_neurons': 40}\n",
+      "0.430847 (0.114088) with: {'dropout': False, 'hidden_activation': 'sigmoid', 'hidden_layers': 2, 'hidden_neurons': 10}\n",
+      "0.504859 (0.038705) with: {'dropout': False, 'hidden_activation': 'sigmoid', 'hidden_layers': 2, 'hidden_neurons': 20}\n",
+      "0.488079 (0.029466) with: {'dropout': False, 'hidden_activation': 'sigmoid', 'hidden_layers': 2, 'hidden_neurons': 30}\n",
+      "0.538757 (0.004428) with: {'dropout': False, 'hidden_activation': 'sigmoid', 'hidden_layers': 2, 'hidden_neurons': 40}\n",
+      "0.491525 (0.038078) with: {'dropout': False, 'hidden_activation': 'sigmoid', 'hidden_layers': 3, 'hidden_neurons': 10}\n",
+      "0.521808 (0.032344) with: {'dropout': False, 'hidden_activation': 'sigmoid', 'hidden_layers': 3, 'hidden_neurons': 20}\n",
+      "0.521808 (0.032344) with: {'dropout': False, 'hidden_activation': 'sigmoid', 'hidden_layers': 3, 'hidden_neurons': 30}\n",
+      "0.525424 (0.029586) with: {'dropout': False, 'hidden_activation': 'sigmoid', 'hidden_layers': 3, 'hidden_neurons': 40}\n",
+      "0.377571 (0.109525) with: {'dropout': False, 'hidden_activation': 'tanh', 'hidden_layers': 1, 'hidden_neurons': 10}\n",
+      "0.529492 (0.151022) with: {'dropout': False, 'hidden_activation': 'tanh', 'hidden_layers': 1, 'hidden_neurons': 20}\n",
+      "0.502147 (0.143221) with: {'dropout': False, 'hidden_activation': 'tanh', 'hidden_layers': 1, 'hidden_neurons': 30}\n",
+      "0.632486 (0.109293) with: {'dropout': False, 'hidden_activation': 'tanh', 'hidden_layers': 1, 'hidden_neurons': 40}\n",
+      "0.448418 (0.153153) with: {'dropout': False, 'hidden_activation': 'tanh', 'hidden_layers': 2, 'hidden_neurons': 10}\n",
+      "0.524237 (0.178769) with: {'dropout': False, 'hidden_activation': 'tanh', 'hidden_layers': 2, 'hidden_neurons': 20}\n",
+      "0.585819 (0.173445) with: {'dropout': False, 'hidden_activation': 'tanh', 'hidden_layers': 2, 'hidden_neurons': 30}\n",
+      "0.780734 (0.064420) with: {'dropout': False, 'hidden_activation': 'tanh', 'hidden_layers': 2, 'hidden_neurons': 40}\n",
+      "0.605819 (0.166289) with: {'dropout': False, 'hidden_activation': 'tanh', 'hidden_layers': 3, 'hidden_neurons': 10}\n",
+      "0.662712 (0.226363) with: {'dropout': False, 'hidden_activation': 'tanh', 'hidden_layers': 3, 'hidden_neurons': 20}\n",
+      "0.764068 (0.033599) with: {'dropout': False, 'hidden_activation': 'tanh', 'hidden_layers': 3, 'hidden_neurons': 30}\n",
+      "0.797853 (0.059259) with: {'dropout': False, 'hidden_activation': 'tanh', 'hidden_layers': 3, 'hidden_neurons': 40}\n"
+     ]
+    }
+   ],
+   "source": [
+    "# summarize results\n",
+    "print(\"Best: %f using %s\" % (grid_result.best_score_, grid_result.best_params_))\n",
+    "means = grid_result.cv_results_['mean_test_score']\n",
+    "stds = grid_result.cv_results_['std_test_score']\n",
+    "params = grid_result.cv_results_['params']\n",
+    "for mean, stdev, param in zip(means, stds, params):\n",
+    "    print(\"%f (%f) with: %r\" % (mean, stdev, param))"
+   ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "dsaKernel",
    "language": "python",
-   "name": "python3"
+   "name": "dsakernel"
   },
   "language_info": {
    "codemirror_mode": {
@@ -840,7 +2075,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.7"
+   "version": "3.12.3"
   }
  },
  "nbformat": 4,
diff --git a/data/dataset_cleaned.csv b/data/dataset_cleaned.csv
new file mode 100644
index 0000000..4a97f6b
--- /dev/null
+++ b/data/dataset_cleaned.csv
@@ -0,0 +1,298 @@
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diff --git a/data/experiments/2024-06-29T15-18-20/fold0model.keras b/data/experiments/2024-06-29T15-18-20/fold0model.keras
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diff --git a/data/experiments/2024-06-29T15-18-20/fold1model.keras b/data/experiments/2024-06-29T15-18-20/fold1model.keras
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diff --git a/data/experiments/2024-06-29T15-18-20/fold5model.keras b/data/experiments/2024-06-29T15-18-20/fold5model.keras
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diff --git a/data/experiments/2024-06-29T15-18-20/fold8model.keras b/data/experiments/2024-06-29T15-18-20/fold8model.keras
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diff --git a/data/experiments/2024-06-29T15-18-20/fold9model.keras b/data/experiments/2024-06-29T15-18-20/fold9model.keras
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