diff --git a/POET_Training.ipynb b/POET_Training.ipynb
index 5f51402..5942f4c 100644
--- a/POET_Training.ipynb
+++ b/POET_Training.ipynb
@@ -27,14 +27,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2025-01-15 14:03:45.613137: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:477] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
+      "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
+      "E0000 00:00:1736946225.710416    5544 cuda_dnn.cc:8310] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
+      "E0000 00:00:1736946225.737063    5544 cuda_blas.cc:1418] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
+      "2025-01-15 14:03:45.952881: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
+      "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Running Keras in version 3.6.0\n"
+      "Running Keras in version 3.7.0\n"
      ]
     }
    ],
@@ -50,7 +62,10 @@
     "import matplotlib.pyplot as plt\n",
     "from sklearn.cluster import KMeans\n",
     "from imblearn.over_sampling import SMOTE\n",
-    "from collections import Counter"
+    "from imblearn.under_sampling import RandomUnderSampler\n",
+    "from imblearn.over_sampling import RandomOverSampler\n",
+    "from collections import Counter\n",
+    "import os"
    ]
   },
   {
@@ -62,9 +77,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 116,
+   "execution_count": 2,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "I0000 00:00:1736946248.679800    5544 gpu_device.cc:2022] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13393 MB memory:  -> device: 0, name: NVIDIA A2, pci bus id: 0000:82:00.0, compute capability: 8.6\n"
+     ]
+    }
+   ],
    "source": [
     "dtype = \"float32\"\n",
     "activation = \"relu\"\n",
@@ -95,7 +118,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -196,17 +219,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 117,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_4\"</span>\n",
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_1\"</span>\n",
        "</pre>\n"
       ],
       "text/plain": [
-       "\u001b[1mModel: \"sequential_4\"\u001b[0m\n"
+       "\u001b[1mModel: \"sequential_1\"\u001b[0m\n"
       ]
      },
      "metadata": {},
@@ -218,13 +241,13 @@
        "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
        "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
        "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
-       "│ dense_15 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)            │         <span style=\"color: #00af00; text-decoration-color: #00af00\">6,656</span> │\n",
+       "│ dense_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)            │         <span style=\"color: #00af00; text-decoration-color: #00af00\">6,656</span> │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_16 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1024</span>)           │       <span style=\"color: #00af00; text-decoration-color: #00af00\">525,312</span> │\n",
+       "│ dense_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1024</span>)           │       <span style=\"color: #00af00; text-decoration-color: #00af00\">525,312</span> │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_17 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)            │       <span style=\"color: #00af00; text-decoration-color: #00af00\">524,800</span> │\n",
+       "│ dense_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)            │       <span style=\"color: #00af00; text-decoration-color: #00af00\">524,800</span> │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_18 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>)             │         <span style=\"color: #00af00; text-decoration-color: #00af00\">6,156</span> │\n",
+       "│ dense_6 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>)             │         <span style=\"color: #00af00; text-decoration-color: #00af00\">6,156</span> │\n",
        "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
        "</pre>\n"
       ],
@@ -232,13 +255,13 @@
        "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
        "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
        "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
-       "│ dense_15 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m)            │         \u001b[38;5;34m6,656\u001b[0m │\n",
+       "│ dense_3 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m)            │         \u001b[38;5;34m6,656\u001b[0m │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_16 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\u001b[0m)           │       \u001b[38;5;34m525,312\u001b[0m │\n",
+       "│ dense_4 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\u001b[0m)           │       \u001b[38;5;34m525,312\u001b[0m │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_17 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m)            │       \u001b[38;5;34m524,800\u001b[0m │\n",
+       "│ dense_5 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m)            │       \u001b[38;5;34m524,800\u001b[0m │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_18 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m)             │         \u001b[38;5;34m6,156\u001b[0m │\n",
+       "│ dense_6 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m)             │         \u001b[38;5;34m6,156\u001b[0m │\n",
        "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
       ]
      },
@@ -377,10 +400,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 86,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
+    "os.chdir('/mnt/beegfs/home/signer/projects/model-training')\n",
     "data_file = h5py.File(\"Barite_50_Data_training.h5\")\n",
     "\n",
     "design = data_file[\"design\"]\n",
@@ -401,14 +425,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 87,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/home/signer/bin/miniconda3/envs/training/lib/python3.11/site-packages/sklearn/base.py:1473: ConvergenceWarning: Number of distinct clusters (1) found smaller than n_clusters (2). Possibly due to duplicate points in X.\n",
+      "/mnt/beegfs/home/signer/.conda/envs/ai/lib/python3.11/site-packages/sklearn/base.py:1473: ConvergenceWarning: Number of distinct clusters (1) found smaller than n_clusters (2). Possibly due to duplicate points in X.\n",
       "  return fit_method(estimator, *args, **kwargs)\n"
      ]
     }
@@ -439,7 +463,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 88,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -452,22 +476,42 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 89,
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Amount class 0: 0.9879380619380619\n",
+      "Amount class 1: 0.012061938061938062\n"
+     ]
+    }
+   ],
+   "source": [
+    "counter = Counter(df_design.iloc[:,-1])\n",
+    "print(\"Amount class 0:\", counter[0] / (counter[0] + counter[1]) )\n",
+    "print(\"Amount class 1:\", counter[1] / (counter[0] + counter[1]) )\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.contour.QuadContourSet at 0x7aa0eb5a9e90>"
+       "<matplotlib.contour.QuadContourSet at 0x7f55a73728d0>"
       ]
      },
-     "execution_count": 89,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -477,7 +521,7 @@
     }
    ],
    "source": [
-    "i=1000\n",
+    "i=800\n",
     "\n",
     "plt.imshow(np.array(df_results['Barite'][(i*2500):(i*2500+2500)]).reshape(50,50), interpolation='bicubic', origin='lower')\n",
     "plt.contour(np.array(df_results['Class'][(i*2500):(i*2500+2500)]).reshape(50,50), levels=[0.1], colors='red', origin='lower')"
@@ -492,7 +536,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 90,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -503,30 +547,59 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Perform SMOT Sampling on dataset to balance classes"
+    "## Perform Over and Under Sampling on dataset to balance classes"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 91,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
-    "Counter(df_design['Class'])\n",
-    "oversample = SMOTE()\n",
+    "sampling_strategy = 'none'\n",
     "\n",
-    "design_resampled, design_classes_resampled = oversample.fit_resample(X_train.iloc[:, :-1], X_train.iloc[:, -1])\n",
-    "target_resampled, target_classes_resampled = oversample.fit_resample(y_train.iloc[:, :-1], y_train.iloc[:, -1])\n"
+    "Counter(df_design['Class'])\n",
+    "\n",
+    "if sampling_strategy == 'smote':\n",
+    "    print(\"Using SMOTE strategy\")\n",
+    "    over = SMOTE(sampling_strategy=0.1)\n",
+    "    design_resampled, design_classes_resampled = over.fit_resample(X_train.iloc[:, :-1], X_train.iloc[:, -1])\n",
+    "    target_resampled, target_classes_resampled = over.fit_resample(y_train.iloc[:, :-1], y_train.iloc[:, -1])\n",
+    "    \n",
+    "elif sampling_strategy == 'over':\n",
+    "    print(\"Using Oversampling\")\n",
+    "    over = RandomOverSampler()\n",
+    "    design_resampled, design_classes_resampled = over.fit_resample(X_train.iloc[:, :-1], X_train.iloc[:, -1])\n",
+    "    target_resampled, target_classes_resampled = over.fit_resample(y_train.iloc[:, :-1], y_train.iloc[:, -1])\n",
+    "    \n",
+    "elif sampling_strategy == 'over/under':\n",
+    "    pass\n",
+    "elif sampling_strategy == 'none':\n",
+    "    pass\n",
+    "\n",
+    "if(sampling_strategy != 'none'):\n",
+    "    X_train = pd.concat([design_resampled, design_classes_resampled], axis=1)\n",
+    "    y_train = pd.concat([target_resampled, target_classes_resampled], axis=1)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 94,
+   "execution_count": 14,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Amount class 0: 0.9879670329670329\n",
+      "Amount class 1: 0.012032967032967032\n"
+     ]
+    }
+   ],
    "source": [
-    "X_train = pd.concat([design_resampled, design_classes_resampled], axis=1)\n",
-    "y_train = pd.concat([target_resampled, target_classes_resampled], axis=1)"
+    "counter = Counter(X_train.iloc[:,-1])\n",
+    "print(\"Amount class 0:\", counter[0] / (counter[0] + counter[1]) )\n",
+    "print(\"Amount class 1:\", counter[1] / (counter[0] + counter[1]) )"
    ]
   },
   {
@@ -538,7 +611,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 96,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -572,7 +645,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -582,7 +655,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 101,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -592,7 +665,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 102,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -602,7 +675,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 100,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -642,7 +715,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 103,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -662,7 +735,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 105,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -692,26 +765,6 @@
     "    return loss"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 106,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(3559968, 12)"
-      ]
-     },
-     "execution_count": 106,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "X_train.iloc[:, :-1].shape"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -721,19 +774,159 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 118,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 1/50\n",
-      "\u001b[1m6954/6954\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m91s\u001b[0m 13ms/step - loss: 0.0070 - val_loss: 0.0066\n",
+      "Epoch 1/50\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
+      "I0000 00:00:1736946361.112525    5608 service.cc:148] XLA service 0x7f55280081d0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n",
+      "I0000 00:00:1736946361.112832    5608 service.cc:156]   StreamExecutor device (0): NVIDIA A2, Compute Capability 8.6\n",
+      "2025-01-15 14:06:01.159888: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:268] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.\n",
+      "I0000 00:00:1736946361.348701    5608 cuda_dnn.cc:529] Loaded cuDNN version 90300\n",
+      "2025-01-15 14:06:01.387004: W external/local_xla/xla/service/gpu/autotuning/gemm_fusion_autotuner.cc:868] Compiling 182 configs for 6 fusions on a single thread.\n",
+      "2025-01-15 14:06:03.621770: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:397] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_163', 760 bytes spill stores, 708 bytes spill loads\n",
+      "\n",
+      "2025-01-15 14:06:10.257275: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:397] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_163', 188 bytes spill stores, 188 bytes spill loads\n",
+      "\n",
+      "2025-01-15 14:06:12.491063: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:397] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_170_0', 776 bytes spill stores, 724 bytes spill loads\n",
+      "\n",
+      "2025-01-15 14:06:14.767081: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:397] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_170', 220 bytes spill stores, 220 bytes spill loads\n",
+      "\n",
+      "2025-01-15 14:06:19.678342: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:397] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_170', 172 bytes spill stores, 384 bytes spill loads\n",
+      "\n",
+      "2025-01-15 14:06:28.467581: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:397] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_343', 664 bytes spill stores, 636 bytes spill loads\n",
+      "\n",
+      "2025-01-15 14:06:35.654758: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:397] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_343', 28 bytes spill stores, 28 bytes spill loads\n",
+      "\n",
+      "2025-01-15 14:06:37.946914: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:397] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_345', 664 bytes spill stores, 636 bytes spill loads\n",
+      "\n",
+      "2025-01-15 14:06:45.130159: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:397] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_345', 28 bytes spill stores, 28 bytes spill loads\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m  59/3520\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m9s\u001b[0m 3ms/step - loss: 0.0151   "
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "I0000 00:00:1736946413.659915    5608 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m3510/3520\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 5.7823e-04"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2025-01-15 14:07:02.606589: W external/local_xla/xla/service/gpu/autotuning/gemm_fusion_autotuner.cc:868] Compiling 170 configs for 6 fusions on a single thread.\n",
+      "2025-01-15 14:07:04.595726: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:397] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_163_0', 756 bytes spill stores, 444 bytes spill loads\n",
+      "\n",
+      "2025-01-15 14:07:11.174922: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:397] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_163_0', 56 bytes spill stores, 68 bytes spill loads\n",
+      "\n",
+      "2025-01-15 14:07:11.599286: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:397] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_163', 12 bytes spill stores, 12 bytes spill loads\n",
+      "\n",
+      "2025-01-15 14:07:12.141013: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:397] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_163', 204 bytes spill stores, 204 bytes spill loads\n",
+      "\n",
+      "2025-01-15 14:07:14.292978: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:397] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_170_0', 756 bytes spill stores, 444 bytes spill loads\n",
+      "\n",
+      "2025-01-15 14:07:15.902316: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:397] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_170', 4 bytes spill stores, 4 bytes spill loads\n",
+      "\n",
+      "2025-01-15 14:07:16.549503: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:397] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_170', 236 bytes spill stores, 236 bytes spill loads\n",
+      "\n",
+      "2025-01-15 14:07:21.435252: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:397] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_170', 12 bytes spill stores, 12 bytes spill loads\n",
+      "\n",
+      "2025-01-15 14:07:22.296584: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:397] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_170', 428 bytes spill stores, 704 bytes spill loads\n",
+      "\n",
+      "2025-01-15 14:07:30.646925: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:397] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_343', 776 bytes spill stores, 792 bytes spill loads\n",
+      "\n",
+      "2025-01-15 14:07:34.111635: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:397] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_343', 92 bytes spill stores, 92 bytes spill loads\n",
+      "\n",
+      "2025-01-15 14:07:34.831349: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:397] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_343', 88 bytes spill stores, 88 bytes spill loads\n",
+      "\n",
+      "2025-01-15 14:07:36.594341: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:397] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_343', 20 bytes spill stores, 28 bytes spill loads\n",
+      "\n",
+      "2025-01-15 14:07:37.378454: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:397] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_343', 32 bytes spill stores, 56 bytes spill loads\n",
+      "\n",
+      "2025-01-15 14:07:39.469100: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:397] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_343', 52 bytes spill stores, 72 bytes spill loads\n",
+      "\n",
+      "2025-01-15 14:07:42.877171: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:397] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_345', 776 bytes spill stores, 792 bytes spill loads\n",
+      "\n",
+      "2025-01-15 14:07:46.407965: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:397] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_345', 92 bytes spill stores, 92 bytes spill loads\n",
+      "\n",
+      "2025-01-15 14:07:47.130611: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:397] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_345', 88 bytes spill stores, 88 bytes spill loads\n",
+      "\n",
+      "2025-01-15 14:07:48.883431: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:397] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_345', 20 bytes spill stores, 28 bytes spill loads\n",
+      "\n",
+      "2025-01-15 14:07:49.652582: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:397] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_345', 32 bytes spill stores, 56 bytes spill loads\n",
+      "\n",
+      "2025-01-15 14:07:51.729864: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:397] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_345', 52 bytes spill stores, 72 bytes spill loads\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m3520/3520\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step - loss: 5.7683e-04"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2025-01-15 14:08:00.788132: W external/local_xla/xla/service/gpu/autotuning/gemm_fusion_autotuner.cc:868] Compiling 72 configs for 3 fusions on a single thread.\n",
+      "2025-01-15 14:08:07.801469: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:397] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_31', 220 bytes spill stores, 220 bytes spill loads\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m3520/3520\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m140s\u001b[0m 25ms/step - loss: 5.7669e-04 - val_loss: 2.6423e-06\n",
       "Epoch 2/50\n",
-      "\u001b[1m6954/6954\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m104s\u001b[0m 15ms/step - loss: 0.0066 - val_loss: 0.0066\n",
+      "\u001b[1m3520/3520\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 3ms/step - loss: 4.9671e-06 - val_loss: 2.3180e-06\n",
       "Epoch 3/50\n",
-      "\u001b[1m4644/6954\u001b[0m \u001b[32m━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━\u001b[0m \u001b[1m35s\u001b[0m 15ms/step - loss: 0.0066"
+      "\u001b[1m3520/3520\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 3ms/step - loss: 3.2911e-06 - val_loss: 4.5144e-06\n",
+      "Epoch 4/50\n",
+      "\u001b[1m3520/3520\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 3ms/step - loss: 2.4528e-06 - val_loss: 1.0435e-06\n",
+      "Epoch 5/50\n",
+      "\u001b[1m3520/3520\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 3ms/step - loss: 1.7198e-06 - val_loss: 8.9310e-07\n",
+      "Epoch 6/50\n",
+      "\u001b[1m3520/3520\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 3ms/step - loss: 1.5762e-06 - val_loss: 6.4216e-07\n",
+      "Epoch 7/50\n",
+      "\u001b[1m3520/3520\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 3ms/step - loss: 1.1924e-06 - val_loss: 6.5215e-07\n",
+      "Epoch 8/50\n",
+      "\u001b[1m3520/3520\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 3ms/step - loss: 1.0509e-06 - val_loss: 7.6646e-07\n",
+      "Epoch 9/50\n",
+      "\u001b[1m3520/3520\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 3ms/step - loss: 9.9888e-07 - val_loss: 6.1071e-07\n",
+      "Epoch 10/50\n",
+      "\u001b[1m3520/3520\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 3ms/step - loss: 1.0026e-06 - val_loss: 5.3742e-07\n",
+      "Epoch 11/50\n",
+      "\u001b[1m3520/3520\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 3ms/step - loss: 8.9634e-07 - val_loss: 6.5116e-07\n",
+      "Epoch 12/50\n",
+      "\u001b[1m2968/3520\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 9.6697e-07"
      ]
     },
     {
@@ -743,18 +936,12 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[118], line 4\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# measure time\u001b[39;00m\n\u001b[1;32m      2\u001b[0m start \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mtime()\n\u001b[0;32m----> 4\u001b[0m history \u001b[38;5;241m=\u001b[39m model_large\u001b[38;5;241m.\u001b[39mfit(X_train\u001b[38;5;241m.\u001b[39miloc[:, :\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m], \n\u001b[1;32m      5\u001b[0m             y_train\u001b[38;5;241m.\u001b[39miloc[:, :\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m], \n\u001b[1;32m      6\u001b[0m             batch_size \u001b[38;5;241m=\u001b[39m batch_size, \n\u001b[1;32m      7\u001b[0m             epochs \u001b[38;5;241m=\u001b[39m epochs, \n\u001b[1;32m      8\u001b[0m             validation_data \u001b[38;5;241m=\u001b[39m (X_val\u001b[38;5;241m.\u001b[39miloc[:,:\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m], y_val\u001b[38;5;241m.\u001b[39miloc[:, :\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m])\n\u001b[1;32m      9\u001b[0m )\n\u001b[1;32m     11\u001b[0m end \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mtime()\n\u001b[1;32m     13\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTraining took \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m seconds\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(end \u001b[38;5;241m-\u001b[39m start))\n",
-      "File \u001b[0;32m~/bin/miniconda3/envs/training/lib/python3.11/site-packages/keras/src/utils/traceback_utils.py:117\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    115\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    116\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 117\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m fn(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m    118\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    119\u001b[0m     filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n",
-      "File \u001b[0;32m~/bin/miniconda3/envs/training/lib/python3.11/site-packages/keras/src/backend/tensorflow/trainer.py:320\u001b[0m, in \u001b[0;36mTensorFlowTrainer.fit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq)\u001b[0m\n\u001b[1;32m    318\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m step, iterator \u001b[38;5;129;01min\u001b[39;00m epoch_iterator\u001b[38;5;241m.\u001b[39menumerate_epoch():\n\u001b[1;32m    319\u001b[0m     callbacks\u001b[38;5;241m.\u001b[39mon_train_batch_begin(step)\n\u001b[0;32m--> 320\u001b[0m     logs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtrain_function(iterator)\n\u001b[1;32m    321\u001b[0m     callbacks\u001b[38;5;241m.\u001b[39mon_train_batch_end(step, logs)\n\u001b[1;32m    322\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstop_training:\n",
-      "File \u001b[0;32m~/bin/miniconda3/envs/training/lib/python3.11/site-packages/tensorflow/python/util/traceback_utils.py:150\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    148\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    149\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 150\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m fn(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m    151\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    152\u001b[0m   filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n",
-      "File \u001b[0;32m~/bin/miniconda3/envs/training/lib/python3.11/site-packages/tensorflow/python/eager/polymorphic_function/polymorphic_function.py:833\u001b[0m, in \u001b[0;36mFunction.__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    830\u001b[0m compiler \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mxla\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jit_compile \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnonXla\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    832\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m OptionalXlaContext(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jit_compile):\n\u001b[0;32m--> 833\u001b[0m   result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_call(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwds)\n\u001b[1;32m    835\u001b[0m new_tracing_count \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mexperimental_get_tracing_count()\n\u001b[1;32m    836\u001b[0m without_tracing \u001b[38;5;241m=\u001b[39m (tracing_count \u001b[38;5;241m==\u001b[39m new_tracing_count)\n",
-      "File \u001b[0;32m~/bin/miniconda3/envs/training/lib/python3.11/site-packages/tensorflow/python/eager/polymorphic_function/polymorphic_function.py:878\u001b[0m, in \u001b[0;36mFunction._call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    875\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock\u001b[38;5;241m.\u001b[39mrelease()\n\u001b[1;32m    876\u001b[0m \u001b[38;5;66;03m# In this case we have not created variables on the first call. So we can\u001b[39;00m\n\u001b[1;32m    877\u001b[0m \u001b[38;5;66;03m# run the first trace but we should fail if variables are created.\u001b[39;00m\n\u001b[0;32m--> 878\u001b[0m results \u001b[38;5;241m=\u001b[39m tracing_compilation\u001b[38;5;241m.\u001b[39mcall_function(\n\u001b[1;32m    879\u001b[0m     args, kwds, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_variable_creation_config\n\u001b[1;32m    880\u001b[0m )\n\u001b[1;32m    881\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_created_variables:\n\u001b[1;32m    882\u001b[0m   \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCreating variables on a non-first call to a function\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    883\u001b[0m                    \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m decorated with tf.function.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
-      "File \u001b[0;32m~/bin/miniconda3/envs/training/lib/python3.11/site-packages/tensorflow/python/eager/polymorphic_function/tracing_compilation.py:139\u001b[0m, in \u001b[0;36mcall_function\u001b[0;34m(args, kwargs, tracing_options)\u001b[0m\n\u001b[1;32m    137\u001b[0m bound_args \u001b[38;5;241m=\u001b[39m function\u001b[38;5;241m.\u001b[39mfunction_type\u001b[38;5;241m.\u001b[39mbind(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m    138\u001b[0m flat_inputs \u001b[38;5;241m=\u001b[39m function\u001b[38;5;241m.\u001b[39mfunction_type\u001b[38;5;241m.\u001b[39munpack_inputs(bound_args)\n\u001b[0;32m--> 139\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m function\u001b[38;5;241m.\u001b[39m_call_flat(  \u001b[38;5;66;03m# pylint: disable=protected-access\u001b[39;00m\n\u001b[1;32m    140\u001b[0m     flat_inputs, captured_inputs\u001b[38;5;241m=\u001b[39mfunction\u001b[38;5;241m.\u001b[39mcaptured_inputs\n\u001b[1;32m    141\u001b[0m )\n",
-      "File \u001b[0;32m~/bin/miniconda3/envs/training/lib/python3.11/site-packages/tensorflow/python/eager/polymorphic_function/concrete_function.py:1322\u001b[0m, in \u001b[0;36mConcreteFunction._call_flat\u001b[0;34m(self, tensor_inputs, captured_inputs)\u001b[0m\n\u001b[1;32m   1318\u001b[0m possible_gradient_type \u001b[38;5;241m=\u001b[39m gradients_util\u001b[38;5;241m.\u001b[39mPossibleTapeGradientTypes(args)\n\u001b[1;32m   1319\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (possible_gradient_type \u001b[38;5;241m==\u001b[39m gradients_util\u001b[38;5;241m.\u001b[39mPOSSIBLE_GRADIENT_TYPES_NONE\n\u001b[1;32m   1320\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m executing_eagerly):\n\u001b[1;32m   1321\u001b[0m   \u001b[38;5;66;03m# No tape is watching; skip to running the function.\u001b[39;00m\n\u001b[0;32m-> 1322\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_inference_function\u001b[38;5;241m.\u001b[39mcall_preflattened(args)\n\u001b[1;32m   1323\u001b[0m forward_backward \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_select_forward_and_backward_functions(\n\u001b[1;32m   1324\u001b[0m     args,\n\u001b[1;32m   1325\u001b[0m     possible_gradient_type,\n\u001b[1;32m   1326\u001b[0m     executing_eagerly)\n\u001b[1;32m   1327\u001b[0m forward_function, args_with_tangents \u001b[38;5;241m=\u001b[39m forward_backward\u001b[38;5;241m.\u001b[39mforward()\n",
-      "File \u001b[0;32m~/bin/miniconda3/envs/training/lib/python3.11/site-packages/tensorflow/python/eager/polymorphic_function/atomic_function.py:216\u001b[0m, in \u001b[0;36mAtomicFunction.call_preflattened\u001b[0;34m(self, args)\u001b[0m\n\u001b[1;32m    214\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mcall_preflattened\u001b[39m(\u001b[38;5;28mself\u001b[39m, args: Sequence[core\u001b[38;5;241m.\u001b[39mTensor]) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Any:\n\u001b[1;32m    215\u001b[0m \u001b[38;5;250m  \u001b[39m\u001b[38;5;124;03m\"\"\"Calls with flattened tensor inputs and returns the structured output.\"\"\"\u001b[39;00m\n\u001b[0;32m--> 216\u001b[0m   flat_outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcall_flat(\u001b[38;5;241m*\u001b[39margs)\n\u001b[1;32m    217\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfunction_type\u001b[38;5;241m.\u001b[39mpack_output(flat_outputs)\n",
-      "File \u001b[0;32m~/bin/miniconda3/envs/training/lib/python3.11/site-packages/tensorflow/python/eager/polymorphic_function/atomic_function.py:251\u001b[0m, in \u001b[0;36mAtomicFunction.call_flat\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m    249\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m record\u001b[38;5;241m.\u001b[39mstop_recording():\n\u001b[1;32m    250\u001b[0m   \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_bound_context\u001b[38;5;241m.\u001b[39mexecuting_eagerly():\n\u001b[0;32m--> 251\u001b[0m     outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_bound_context\u001b[38;5;241m.\u001b[39mcall_function(\n\u001b[1;32m    252\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mname,\n\u001b[1;32m    253\u001b[0m         \u001b[38;5;28mlist\u001b[39m(args),\n\u001b[1;32m    254\u001b[0m         \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfunction_type\u001b[38;5;241m.\u001b[39mflat_outputs),\n\u001b[1;32m    255\u001b[0m     )\n\u001b[1;32m    256\u001b[0m   \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    257\u001b[0m     outputs \u001b[38;5;241m=\u001b[39m make_call_op_in_graph(\n\u001b[1;32m    258\u001b[0m         \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m    259\u001b[0m         \u001b[38;5;28mlist\u001b[39m(args),\n\u001b[1;32m    260\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_bound_context\u001b[38;5;241m.\u001b[39mfunction_call_options\u001b[38;5;241m.\u001b[39mas_attrs(),\n\u001b[1;32m    261\u001b[0m     )\n",
-      "File \u001b[0;32m~/bin/miniconda3/envs/training/lib/python3.11/site-packages/tensorflow/python/eager/context.py:1552\u001b[0m, in \u001b[0;36mContext.call_function\u001b[0;34m(self, name, tensor_inputs, num_outputs)\u001b[0m\n\u001b[1;32m   1550\u001b[0m cancellation_context \u001b[38;5;241m=\u001b[39m cancellation\u001b[38;5;241m.\u001b[39mcontext()\n\u001b[1;32m   1551\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m cancellation_context \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m-> 1552\u001b[0m   outputs \u001b[38;5;241m=\u001b[39m execute\u001b[38;5;241m.\u001b[39mexecute(\n\u001b[1;32m   1553\u001b[0m       name\u001b[38;5;241m.\u001b[39mdecode(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mutf-8\u001b[39m\u001b[38;5;124m\"\u001b[39m),\n\u001b[1;32m   1554\u001b[0m       num_outputs\u001b[38;5;241m=\u001b[39mnum_outputs,\n\u001b[1;32m   1555\u001b[0m       inputs\u001b[38;5;241m=\u001b[39mtensor_inputs,\n\u001b[1;32m   1556\u001b[0m       attrs\u001b[38;5;241m=\u001b[39mattrs,\n\u001b[1;32m   1557\u001b[0m       ctx\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m   1558\u001b[0m   )\n\u001b[1;32m   1559\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   1560\u001b[0m   outputs \u001b[38;5;241m=\u001b[39m execute\u001b[38;5;241m.\u001b[39mexecute_with_cancellation(\n\u001b[1;32m   1561\u001b[0m       name\u001b[38;5;241m.\u001b[39mdecode(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mutf-8\u001b[39m\u001b[38;5;124m\"\u001b[39m),\n\u001b[1;32m   1562\u001b[0m       num_outputs\u001b[38;5;241m=\u001b[39mnum_outputs,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1566\u001b[0m       cancellation_manager\u001b[38;5;241m=\u001b[39mcancellation_context,\n\u001b[1;32m   1567\u001b[0m   )\n",
-      "File \u001b[0;32m~/bin/miniconda3/envs/training/lib/python3.11/site-packages/tensorflow/python/eager/execute.py:53\u001b[0m, in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m     51\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m     52\u001b[0m   ctx\u001b[38;5;241m.\u001b[39mensure_initialized()\n\u001b[0;32m---> 53\u001b[0m   tensors \u001b[38;5;241m=\u001b[39m pywrap_tfe\u001b[38;5;241m.\u001b[39mTFE_Py_Execute(ctx\u001b[38;5;241m.\u001b[39m_handle, device_name, op_name,\n\u001b[1;32m     54\u001b[0m                                       inputs, attrs, num_outputs)\n\u001b[1;32m     55\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m core\u001b[38;5;241m.\u001b[39m_NotOkStatusException \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m     56\u001b[0m   \u001b[38;5;28;01mif\u001b[39;00m name \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
+      "Cell \u001b[0;32mIn[21], line 4\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# measure time\u001b[39;00m\n\u001b[1;32m      2\u001b[0m start \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mtime()\n\u001b[0;32m----> 4\u001b[0m history \u001b[38;5;241m=\u001b[39m model_large\u001b[38;5;241m.\u001b[39mfit(X_train\u001b[38;5;241m.\u001b[39miloc[:, :\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m], \n\u001b[1;32m      5\u001b[0m             y_train\u001b[38;5;241m.\u001b[39miloc[:, :\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m], \n\u001b[1;32m      6\u001b[0m             batch_size \u001b[38;5;241m=\u001b[39m batch_size, \n\u001b[1;32m      7\u001b[0m             epochs \u001b[38;5;241m=\u001b[39m epochs, \n\u001b[1;32m      8\u001b[0m             validation_data \u001b[38;5;241m=\u001b[39m (X_val\u001b[38;5;241m.\u001b[39miloc[:,:\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m], y_val\u001b[38;5;241m.\u001b[39miloc[:, :\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m])\n\u001b[1;32m      9\u001b[0m )\n\u001b[1;32m     11\u001b[0m end \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mtime()\n\u001b[1;32m     13\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTraining took \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m seconds\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(end \u001b[38;5;241m-\u001b[39m start))\n",
+      "File \u001b[0;32m~/.conda/envs/ai/lib/python3.11/site-packages/keras/src/utils/traceback_utils.py:117\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    115\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    116\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 117\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m fn(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m    118\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    119\u001b[0m     filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n",
+      "File \u001b[0;32m~/.conda/envs/ai/lib/python3.11/site-packages/keras/src/backend/tensorflow/trainer.py:368\u001b[0m, in \u001b[0;36mTensorFlowTrainer.fit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq)\u001b[0m\n\u001b[1;32m    366\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m step, iterator \u001b[38;5;129;01min\u001b[39;00m epoch_iterator:\n\u001b[1;32m    367\u001b[0m     callbacks\u001b[38;5;241m.\u001b[39mon_train_batch_begin(step)\n\u001b[0;32m--> 368\u001b[0m     logs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtrain_function(iterator)\n\u001b[1;32m    369\u001b[0m     callbacks\u001b[38;5;241m.\u001b[39mon_train_batch_end(step, logs)\n\u001b[1;32m    370\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstop_training:\n",
+      "File \u001b[0;32m~/.conda/envs/ai/lib/python3.11/site-packages/keras/src/backend/tensorflow/trainer.py:217\u001b[0m, in \u001b[0;36mTensorFlowTrainer._make_function.<locals>.function\u001b[0;34m(iterator)\u001b[0m\n\u001b[1;32m    213\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\n\u001b[1;32m    214\u001b[0m     iterator, (tf\u001b[38;5;241m.\u001b[39mdata\u001b[38;5;241m.\u001b[39mIterator, tf\u001b[38;5;241m.\u001b[39mdistribute\u001b[38;5;241m.\u001b[39mDistributedIterator)\n\u001b[1;32m    215\u001b[0m ):\n\u001b[1;32m    216\u001b[0m     opt_outputs \u001b[38;5;241m=\u001b[39m multi_step_on_iterator(iterator)\n\u001b[0;32m--> 217\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m opt_outputs\u001b[38;5;241m.\u001b[39mhas_value():\n\u001b[1;32m    218\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mStopIteration\u001b[39;00m\n\u001b[1;32m    219\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m opt_outputs\u001b[38;5;241m.\u001b[39mget_value()\n",
+      "File \u001b[0;32m~/.conda/envs/ai/lib/python3.11/site-packages/tensorflow/python/data/ops/optional_ops.py:176\u001b[0m, in \u001b[0;36m_OptionalImpl.has_value\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m    174\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mhas_value\u001b[39m(\u001b[38;5;28mself\u001b[39m, name\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m    175\u001b[0m   \u001b[38;5;28;01mwith\u001b[39;00m ops\u001b[38;5;241m.\u001b[39mcolocate_with(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_variant_tensor):\n\u001b[0;32m--> 176\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m gen_optional_ops\u001b[38;5;241m.\u001b[39moptional_has_value(\n\u001b[1;32m    177\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_variant_tensor, name\u001b[38;5;241m=\u001b[39mname\n\u001b[1;32m    178\u001b[0m     )\n",
+      "File \u001b[0;32m~/.conda/envs/ai/lib/python3.11/site-packages/tensorflow/python/ops/gen_optional_ops.py:172\u001b[0m, in \u001b[0;36moptional_has_value\u001b[0;34m(optional, name)\u001b[0m\n\u001b[1;32m    170\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m tld\u001b[38;5;241m.\u001b[39mis_eager:\n\u001b[1;32m    171\u001b[0m   \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 172\u001b[0m     _result \u001b[38;5;241m=\u001b[39m pywrap_tfe\u001b[38;5;241m.\u001b[39mTFE_Py_FastPathExecute(\n\u001b[1;32m    173\u001b[0m       _ctx, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mOptionalHasValue\u001b[39m\u001b[38;5;124m\"\u001b[39m, name, optional)\n\u001b[1;32m    174\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m _result\n\u001b[1;32m    175\u001b[0m   \u001b[38;5;28;01mexcept\u001b[39;00m _core\u001b[38;5;241m.\u001b[39m_NotOkStatusException \u001b[38;5;28;01mas\u001b[39;00m e:\n",
       "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
      ]
     }
@@ -1212,7 +1399,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "training",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },