deep_learning_transformer_only.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-30T11:11:44.622367Z",
     "iopub.status.busy": "2024-08-30T11:11:44.622295Z",
     "iopub.status.idle": "2024-08-30T11:11:44.624677Z",
     "shell.execute_reply": "2024-08-30T11:11:44.624521Z"
    }
   },
   "outputs": [],
   "source": [
    "splitPartCount = 5\n",
    "splitSeed = 27\n",
    "hoursPerWindow = 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preproccess"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## read data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-30T11:11:44.625596Z",
     "iopub.status.busy": "2024-08-30T11:11:44.625534Z",
     "iopub.status.idle": "2024-08-30T11:11:45.759519Z",
     "shell.execute_reply": "2024-08-30T11:11:45.759339Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1213"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from utils.class_patient import Patients\n",
    "\n",
    "\n",
    "patients = Patients.loadPatients()\n",
    "len(patients)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## remove missing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-30T11:11:45.773343Z",
     "iopub.status.busy": "2024-08-30T11:11:45.773174Z",
     "iopub.status.idle": "2024-08-30T11:11:45.782738Z",
     "shell.execute_reply": "2024-08-30T11:11:45.782553Z"
    }
   },
   "outputs": [],
   "source": [
    "# fill measures whose null represent false value\n",
    "\n",
    "from constants import NULLABLE_MEASURES\n",
    "\n",
    "\n",
    "nullableMeasures = NULLABLE_MEASURES\n",
    "\n",
    "for measureName in nullableMeasures:\n",
    "    patients.fillMissingMeasureValue(measureName, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-30T11:11:45.783825Z",
     "iopub.status.busy": "2024-08-30T11:11:45.783731Z",
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     "shell.execute_reply": "2024-08-30T11:11:45.795913Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pco2 917\n",
      "ph 954\n",
      "po2 917\n",
      "albumin 406\n",
      "hba1c 326\n",
      "lymphocyte 446\n",
      "height 415\n",
      "urine-ketone 294\n",
      "crp 19\n"
     ]
    }
   ],
   "source": [
    "# remove measures with less than 80% of data\n",
    "\n",
    "measures = patients.getMeasures()\n",
    "\n",
    "for measure, count in measures.items():\n",
    "    if count < len(patients) * 80 / 100:\n",
    "        patients.removeMeasures([measure])\n",
    "        print(measure, count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-30T11:11:45.797106Z",
     "iopub.status.busy": "2024-08-30T11:11:45.797039Z",
     "iopub.status.idle": "2024-08-30T11:11:45.801244Z",
     "shell.execute_reply": "2024-08-30T11:11:45.801084Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1206"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# remove patients with less than 80% of data\n",
    "\n",
    "patients.removePatientByMissingFeatures()\n",
    "len(patients)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-30T11:11:45.802195Z",
     "iopub.status.busy": "2024-08-30T11:11:45.802127Z",
     "iopub.status.idle": "2024-08-30T11:11:45.804572Z",
     "shell.execute_reply": "2024-08-30T11:11:45.804424Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "82"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# # remove patients with positive tag in first 12 hours\n",
    "\n",
    "from pandas import Timedelta\n",
    "\n",
    "\n",
    "patients.removePatientAkiEarly(Timedelta(hours=12))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-30T11:11:45.805478Z",
     "iopub.status.busy": "2024-08-30T11:11:45.805418Z",
     "iopub.status.idle": "2024-08-30T11:11:45.807190Z",
     "shell.execute_reply": "2024-08-30T11:11:45.807041Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total  1124\n",
      "AKI  392\n",
      "Ratio  0.3487544483985765\n"
     ]
    }
   ],
   "source": [
    "print(\"Total \", len(patients))\n",
    "print(\"AKI \", sum([1 for p in patients if p.akdPositive]))\n",
    "print(\"Ratio \", sum([1 for p in patients if p.akdPositive]) / len(patients))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## split patients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-30T11:11:45.807927Z",
     "iopub.status.busy": "2024-08-30T11:11:45.807860Z",
     "iopub.status.idle": "2024-08-30T11:11:45.809738Z",
     "shell.execute_reply": "2024-08-30T11:11:45.809594Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "225"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "splitedPatients = patients.split(splitPartCount, splitSeed)\n",
    "\n",
    "len(splitedPatients[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-30T11:11:45.810385Z",
     "iopub.status.busy": "2024-08-30T11:11:45.810327Z",
     "iopub.status.idle": "2024-08-30T11:11:45.812634Z",
     "shell.execute_reply": "2024-08-30T11:11:45.812479Z"
    }
   },
   "outputs": [],
   "source": [
    "splitedPatients = patients.split(splitPartCount, splitSeed)\n",
    "\n",
    "\n",
    "def trainTest():\n",
    "    for i in range(splitedPatients.__len__()):\n",
    "        testPatients = splitedPatients[i]\n",
    "\n",
    "        trainPatientsList = splitedPatients[:i] + splitedPatients[i + 1 :]\n",
    "        trainPatients = Patients(patients=[])\n",
    "        for trainPatientsElem in trainPatientsList:\n",
    "            trainPatients += trainPatientsElem\n",
    "\n",
    "        yield trainPatients, testPatients\n",
    "\n",
    "\n",
    "def trainValTest():\n",
    "    for i in range(splitedPatients.__len__()):\n",
    "        testPatients = splitedPatients[i]\n",
    "\n",
    "        trainPatientsList = splitedPatients[:i] + splitedPatients[i + 1 :]\n",
    "        trainPatients = Patients(patients=[])\n",
    "        for trainPatientsElem in trainPatientsList:\n",
    "            trainPatients += trainPatientsElem\n",
    "\n",
    "        *trainPatients, valPatients = trainPatients.split(5, 27)\n",
    "        tmpPatients = Patients(patients=[])\n",
    "        for trainPatientsElem in trainPatients:\n",
    "            tmpPatients += trainPatientsElem\n",
    "        trainPatients = tmpPatients\n",
    "\n",
    "        yield trainPatients, valPatients, testPatients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-30T11:11:45.813262Z",
     "iopub.status.busy": "2024-08-30T11:11:45.813203Z",
     "iopub.status.idle": "2024-08-30T11:11:45.814757Z",
     "shell.execute_reply": "2024-08-30T11:11:45.814602Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "899 225\n",
      "899 225\n",
      "899 225\n",
      "899 225\n",
      "900 224\n"
     ]
    }
   ],
   "source": [
    "for trainPatients, testPatients in trainTest():\n",
    "    print(len(trainPatients.patientList), len(testPatients.patientList))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Transformer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Transformer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using transformer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-30T11:11:45.815460Z",
     "iopub.status.busy": "2024-08-30T11:11:45.815401Z",
     "iopub.status.idle": "2024-08-30T11:11:46.844847Z",
     "shell.execute_reply": "2024-08-30T11:11:46.844665Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-08-30 18:11:45.890684: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n",
      "2024-08-30 18:11:45.898108: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:485] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
      "2024-08-30 18:11:45.904889: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:8454] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
      "2024-08-30 18:11:45.907289: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1452] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
      "2024-08-30 18:11:45.914091: 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 AVX_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
      "I0000 00:00:1725016306.665021 2579843 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1725016306.669033 2579843 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1725016306.669120 2579843 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1725016306.670476 2579843 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1725016306.670535 2579843 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1725016306.670576 2579843 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1725016306.724686 2579843 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1725016306.724767 2579843 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1725016306.724815 2579843 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "2024-08-30 18:11:46.724858: I tensorflow/core/common_runtime/gpu/gpu_device.cc:2021] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 14415 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.9\n"
     ]
    },
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       "<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: \"array_to_boolean_model\"</span>\n",
       "</pre>\n"
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      "text/plain": [
       "\u001b[1mModel: \"array_to_boolean_model\"\u001b[0m\n"
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       "<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",
       "│ global_average_pooling1d        │ ?                      │   <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (unbuilt) │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">GlobalAveragePooling1D</span>)        │                        │               │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                   │ ?                      │   <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (unbuilt) │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
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       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\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",
       "│ global_average_pooling1d        │ ?                      │   \u001b[38;5;34m0\u001b[0m (unbuilt) │\n",
       "│ (\u001b[38;5;33mGlobalAveragePooling1D\u001b[0m)        │                        │               │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense (\u001b[38;5;33mDense\u001b[0m)                   │ ?                      │   \u001b[38;5;34m0\u001b[0m (unbuilt) │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
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      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
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      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
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      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from official.nlp.modeling.models import TransformerEncoder\n",
    "from official.nlp.configs import encoders\n",
    "\n",
    "# # Configuration for the Transformer encoder\n",
    "# encoder_config = encoders.TransformerEncoderConfig(\n",
    "#     vocab_size=30522,  # Use a large enough vocab size if working with tokenized text; adjust if needed\n",
    "#     hidden_size=128,  # Example hidden size\n",
    "#     num_layers=2,  # Example number of layers\n",
    "#     num_attention_heads=4,  # Example number of attention heads\n",
    "#     intermediate_size=512,  # Size of the feed-forward network's intermediate layer\n",
    "#     activation=\"gelu\",\n",
    "#     dropout_rate=0.1,\n",
    "#     attention_dropout_rate=0.1,\n",
    "#     max_sequence_length=512,  # Max length of input sequences\n",
    "#     type_vocab_size=2,\n",
    "# )\n",
    "\n",
    "import keras\n",
    "\n",
    "@keras.saving.register_keras_serializable()\n",
    "class ArrayToBooleanModel(keras.Model):\n",
    "    def __init__(self, **kwargs):\n",
    "        super(ArrayToBooleanModel, self).__init__(**kwargs)\n",
    "        self.transformer_encoder = TransformerEncoder(\n",
    "            dropout_rate=0.1,\n",
    "            attention_dropout_rate=0.1,\n",
    "            # max_sequence_length=max_sequence_length,\n",
    "            # vocab_size=vocab_size,\n",
    "        )\n",
    "        self.pooling = keras.layers.GlobalAveragePooling1D()\n",
    "        self.dense = keras.layers.Dense(\n",
    "            1, activation=\"sigmoid\"\n",
    "        )  # Output a single boolean value\n",
    "\n",
    "    def call(self, inputs, training=False):\n",
    "        x = self.transformer_encoder(inputs, training=training)\n",
    "        x = self.pooling(x)\n",
    "        x = self.dense(x)\n",
    "        return x\n",
    "\n",
    "\n",
    "def createTransformerModel(timeSteps, features):\n",
    "    from tensorflow.keras.optimizers import Adam\n",
    "    model = ArrayToBooleanModel()\n",
    "\n",
    "    model.compile(\n",
    "        optimizer=Adam(learning_rate=0.0001),\n",
    "        loss=\"binary_crossentropy\",\n",
    "        metrics=[\"AUC\", \"accuracy\", \"precision\", \"recall\"],\n",
    "    )\n",
    "\n",
    "    return model\n",
    "\n",
    "createTransformerModel(12, 21).summary(expand_nested=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-30T11:11:46.845951Z",
     "iopub.status.busy": "2024-08-30T11:11:46.845882Z",
     "iopub.status.idle": "2024-08-30T11:32:28.667945Z",
     "shell.execute_reply": "2024-08-30T11:32:28.667743Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:The input size (82) is not a multiple of the number of attention heads (8)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:The input size (82) is not a multiple of the number of attention heads (8)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:The input size (82) is not a multiple of the number of attention heads (8)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:The input size (82) is not a multiple of the number of attention heads (8)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:The input size (82) is not a multiple of the number of attention heads (8)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:The input size (82) is not a multiple of the number of attention heads (8)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
      "I0000 00:00:1725016309.308607 2579972 service.cc:146] XLA service 0x7364480037e0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n",
      "I0000 00:00:1725016309.308625 2579972 service.cc:154]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9\n",
      "2024-08-30 18:11:49.353913: 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",
      "2024-08-30 18:11:49.373483: W tensorflow/compiler/tf2xla/kernels/random_ops.cc:61] Warning: Using tf.random.uniform with XLA compilation will ignore seeds; consider using tf.random.stateless_uniform instead if reproducible behavior is desired. array_to_boolean_model_1_1/transformer_encoder_1/layer_0/self_attention/dropout_3/dropout/random_uniform/RandomUniform\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-08-30 18:11:49.771954: I external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:531] Loaded cuDNN version 8907\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-08-30 18:11:50.325077: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:393] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_7', 60 bytes spill stores, 60 bytes spill loads\n",
      "\n",
      "2024-08-30 18:11:50.414440: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:393] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_7', 188 bytes spill stores, 188 bytes spill loads\n",
      "\n",
      "2024-08-30 18:11:50.499039: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:393] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_7', 380 bytes spill stores, 328 bytes spill loads\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "I0000 00:00:1725016312.216049 2579972 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-08-30 18:11:53.141649: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:393] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_7', 48 bytes spill stores, 48 bytes spill loads\n",
      "\n",
      "2024-08-30 18:11:53.212317: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:393] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_5', 12 bytes spill stores, 12 bytes spill loads\n",
      "\n",
      "2024-08-30 18:11:53.233280: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:393] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_7', 236 bytes spill stores, 236 bytes spill loads\n",
      "\n",
      "2024-08-30 18:11:53.277495: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:393] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_5', 24 bytes spill stores, 24 bytes spill loads\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-08-30 18:11:53.372904: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:393] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_7', 676 bytes spill stores, 484 bytes spill loads\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-08-30 18:11:56.867034: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:393] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_7', 48 bytes spill stores, 48 bytes spill loads\n",
      "\n",
      "2024-08-30 18:11:56.940675: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:393] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_7', 236 bytes spill stores, 236 bytes spill loads\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-08-30 18:11:57.124775: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:393] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_7', 676 bytes spill stores, 484 bytes spill loads\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[1m1/8\u001b[0m \u001b[32m━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m5s\u001b[0m 842ms/step"
     ]
    },
    {
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "WARNING:absl:The input size (82) is not a multiple of the number of attention heads (8)\n"
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    },
    {
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     "output_type": "stream",
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      "WARNING:absl:The input size (82) is not a multiple of the number of attention heads (8)\n"
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    {
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     "output_type": "stream",
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      "WARNING:absl:The input size (82) is not a multiple of the number of attention heads (8)\n"
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    {
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     "output_type": "stream",
     "text": [
      "WARNING:absl:The input size (82) is not a multiple of the number of attention heads (8)\n"
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    {
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     "output_type": "stream",
     "text": [
      "WARNING:absl:The input size (82) is not a multiple of the number of attention heads (8)\n"
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    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "WARNING:absl:The input size (82) is not a multiple of the number of attention heads (8)\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[1m1/8\u001b[0m \u001b[32m━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m5s\u001b[0m 768ms/step"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 101ms/step"
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 101ms/step\n"
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:The input size (82) is not a multiple of the number of attention heads (8)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:The input size (82) is not a multiple of the number of attention heads (8)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:The input size (82) is not a multiple of the number of attention heads (8)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:The input size (82) is not a multiple of the number of attention heads (8)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:The input size (82) is not a multiple of the number of attention heads (8)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:5 out of the last 17 calls to <function TensorFlowTrainer.make_predict_function.<locals>.one_step_on_data_distributed at 0x73659bfaaf20> 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"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:5 out of the last 17 calls to <function TensorFlowTrainer.make_predict_function.<locals>.one_step_on_data_distributed at 0x73659bfaaf20> 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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[1m1/8\u001b[0m \u001b[32m━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m5s\u001b[0m 775ms/step"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step"
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    {
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     "output_type": "stream",
     "text": [
      "WARNING:absl:The input size (82) is not a multiple of the number of attention heads (8)\n"
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    {
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     "output_type": "stream",
     "text": [
      "WARNING:absl:The input size (82) is not a multiple of the number of attention heads (8)\n"
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    {
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     "output_type": "stream",
     "text": [
      "WARNING:absl:The input size (82) is not a multiple of the number of attention heads (8)\n"
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:The input size (82) is not a multiple of the number of attention heads (8)\n"
     ]
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:The input size (82) is not a multiple of the number of attention heads (8)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:5 out of the last 17 calls to <function TensorFlowTrainer.make_predict_function.<locals>.one_step_on_data_distributed at 0x736598335b20> 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"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:5 out of the last 17 calls to <function TensorFlowTrainer.make_predict_function.<locals>.one_step_on_data_distributed at 0x736598335b20> 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"
     ]
    },
    {
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     "output_type": "stream",
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    {
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     "output_type": "stream",
     "text": [
      "WARNING:absl:The input size (82) is not a multiple of the number of attention heads (8)\n"
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    {
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     "text": [
      "\r",
      "\u001b[1m1/8\u001b[0m \u001b[32m━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m5s\u001b[0m 793ms/step"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 99ms/step "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 99ms/step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-08-30 18:15:09.883951: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:393] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_7', 20 bytes spill stores, 20 bytes spill loads\n",
      "\n",
      "2024-08-30 18:15:09.957352: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:393] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_7', 132 bytes spill stores, 132 bytes spill loads\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-08-30 18:15:10.120259: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:393] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_7', 348 bytes spill stores, 296 bytes spill loads\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-08-30 18:15:12.970319: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:393] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_5', 4 bytes spill stores, 4 bytes spill loads\n",
      "\n",
      "2024-08-30 18:15:13.000972: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:393] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_5', 4 bytes spill stores, 4 bytes spill loads\n",
      "\n",
      "2024-08-30 18:15:13.002390: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:393] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_7', 204 bytes spill stores, 204 bytes spill loads\n",
      "\n",
      "2024-08-30 18:15:13.094450: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:393] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_7', 620 bytes spill stores, 408 bytes spill loads\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-08-30 18:15:16.453052: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:393] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_7', 204 bytes spill stores, 204 bytes spill loads\n",
      "\n",
      "2024-08-30 18:15:16.543886: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:393] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_7', 620 bytes spill stores, 408 bytes spill loads\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[1m1/8\u001b[0m \u001b[32m━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m8s\u001b[0m 1s/step"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 142ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[1m1/8\u001b[0m \u001b[32m━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m5s\u001b[0m 794ms/step"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 101ms/step"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 101ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[1m1/8\u001b[0m \u001b[32m━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m5s\u001b[0m 776ms/step"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 103ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[1m1/8\u001b[0m \u001b[32m━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m5s\u001b[0m 779ms/step"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 102ms/step"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 102ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[1m1/8\u001b[0m \u001b[32m━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m5s\u001b[0m 778ms/step"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 102ms/step"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 102ms/step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:The input size (78) is not a multiple of the number of attention heads (8)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:The input size (78) is not a multiple of the number of attention heads (8)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:The input size (78) is not a multiple of the number of attention heads (8)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:The input size (78) is not a multiple of the number of attention heads (8)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:The input size (78) is not a multiple of the number of attention heads (8)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:The input size (78) is not a multiple of the number of attention heads (8)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-08-30 18:19:56.918442: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:393] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_7', 188 bytes spill stores, 188 bytes spill loads\n",
      "\n",
      "2024-08-30 18:19:56.990145: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:393] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_7', 60 bytes spill stores, 60 bytes spill loads\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-08-30 18:19:57.120227: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:393] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_7', 380 bytes spill stores, 328 bytes spill loads\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-08-30 18:20:00.007314: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:393] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_7', 48 bytes spill stores, 48 bytes spill loads\n",
      "\n",
      "2024-08-30 18:20:00.072490: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:393] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_5', 80 bytes spill stores, 80 bytes spill loads\n",
      "\n",
      "2024-08-30 18:20:00.085224: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:393] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_5', 48 bytes spill stores, 48 bytes spill loads\n",
      "\n",
      "2024-08-30 18:20:00.109978: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:393] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_7', 236 bytes spill stores, 236 bytes spill loads\n",
      "\n",
      "2024-08-30 18:20:00.197106: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:393] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_7', 676 bytes spill stores, 484 bytes spill loads\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-08-30 18:20:03.623591: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:393] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_7', 48 bytes spill stores, 48 bytes spill loads\n",
      "\n",
      "2024-08-30 18:20:03.674920: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:393] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_7', 236 bytes spill stores, 236 bytes spill loads\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-08-30 18:20:03.868237: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:393] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_7', 676 bytes spill stores, 484 bytes spill loads\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[1m1/8\u001b[0m \u001b[32m━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m6s\u001b[0m 870ms/step"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 156ms/step"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 156ms/step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:The input size (78) is not a multiple of the number of attention heads (8)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:The input size (78) is not a multiple of the number of attention heads (8)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:The input size (78) is not a multiple of the number of attention heads (8)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:The input size (78) is not a multiple of the number of attention heads (8)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:The input size (78) is not a multiple of the number of attention heads (8)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:The input size (78) is not a multiple of the number of attention heads (8)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[1m1/8\u001b[0m \u001b[32m━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m5s\u001b[0m 783ms/step"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 104ms/step"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 104ms/step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:The input size (78) is not a multiple of the number of attention heads (8)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:The input size (78) is not a multiple of the number of attention heads (8)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:The input size (78) is not a multiple of the number of attention heads (8)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:The input size (78) is not a multiple of the number of attention heads (8)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:The input size (78) is not a multiple of the number of attention heads (8)\n"
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:The input size (78) is not a multiple of the number of attention heads (8)\n"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[1m1/8\u001b[0m \u001b[32m━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m5s\u001b[0m 758ms/step"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 105ms/step"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 105ms/step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:The input size (78) is not a multiple of the number of attention heads (8)\n"
     ]
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:The input size (78) is not a multiple of the number of attention heads (8)\n"
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    {
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     "text": [
      "WARNING:absl:The input size (78) is not a multiple of the number of attention heads (8)\n"
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     "output_type": "stream",
     "text": [
      "WARNING:absl:The input size (78) is not a multiple of the number of attention heads (8)\n"
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    {
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     "output_type": "stream",
     "text": [
      "WARNING:absl:The input size (78) is not a multiple of the number of attention heads (8)\n"
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:The input size (78) is not a multiple of the number of attention heads (8)\n"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[1m1/8\u001b[0m \u001b[32m━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m5s\u001b[0m 806ms/step"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 101ms/step"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 101ms/step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:The input size (78) is not a multiple of the number of attention heads (8)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:The input size (78) is not a multiple of the number of attention heads (8)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:The input size (78) is not a multiple of the number of attention heads (8)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:The input size (78) is not a multiple of the number of attention heads (8)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:The input size (78) is not a multiple of the number of attention heads (8)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:The input size (78) is not a multiple of the number of attention heads (8)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[1m1/8\u001b[0m \u001b[32m━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m5s\u001b[0m 798ms/step"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 102ms/step"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 102ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[1m1/8\u001b[0m \u001b[32m━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m5s\u001b[0m 802ms/step"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 103ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[1m1/8\u001b[0m \u001b[32m━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m5s\u001b[0m 772ms/step"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 105ms/step"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 105ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[1m1/8\u001b[0m \u001b[32m━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m5s\u001b[0m 784ms/step"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 102ms/step"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 102ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[1m1/8\u001b[0m \u001b[32m━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m5s\u001b[0m 767ms/step"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 104ms/step"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 104ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[1m1/8\u001b[0m \u001b[32m━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m5s\u001b[0m 787ms/step"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 102ms/step"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 102ms/step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:The input size (78) is not a multiple of the number of attention heads (8)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:The input size (78) is not a multiple of the number of attention heads (8)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:The input size (78) is not a multiple of the number of attention heads (8)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:The input size (78) is not a multiple of the number of attention heads (8)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:The input size (78) is not a multiple of the number of attention heads (8)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:The input size (78) is not a multiple of the number of attention heads (8)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-08-30 18:28:10.714435: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:393] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_7', 188 bytes spill stores, 188 bytes spill loads\n",
      "\n",
      "2024-08-30 18:28:10.728090: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:393] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_5', 80 bytes spill stores, 80 bytes spill loads\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-08-30 18:28:10.944470: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:393] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_7', 676 bytes spill stores, 484 bytes spill loads\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[1m1/7\u001b[0m \u001b[32m━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m4s\u001b[0m 798ms/step"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step  \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:The input size (78) is not a multiple of the number of attention heads (8)\n"
     ]
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    {
     "name": "stderr",
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     "text": [
      "WARNING:absl:The input size (78) is not a multiple of the number of attention heads (8)\n"
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:The input size (78) is not a multiple of the number of attention heads (8)\n"
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:The input size (78) is not a multiple of the number of attention heads (8)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:The input size (78) is not a multiple of the number of attention heads (8)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:The input size (78) is not a multiple of the number of attention heads (8)\n"
     ]
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[1m1/7\u001b[0m \u001b[32m━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m4s\u001b[0m 792ms/step"
     ]
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step  \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:The input size (78) is not a multiple of the number of attention heads (8)\n"
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:The input size (78) is not a multiple of the number of attention heads (8)\n"
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     "name": "stderr",
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     "text": [
      "WARNING:absl:The input size (78) is not a multiple of the number of attention heads (8)\n"
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:The input size (78) is not a multiple of the number of attention heads (8)\n"
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:The input size (78) is not a multiple of the number of attention heads (8)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:The input size (78) is not a multiple of the number of attention heads (8)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[1m1/7\u001b[0m \u001b[32m━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m4s\u001b[0m 797ms/step"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step  \n"
     ]
    },
    {
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      "WARNING:absl:The input size (78) is not a multiple of the number of attention heads (8)\n"
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    },
    {
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      "WARNING:absl:The input size (78) is not a multiple of the number of attention heads (8)\n"
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    {
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     "output_type": "stream",
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      "WARNING:absl:The input size (78) is not a multiple of the number of attention heads (8)\n"
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    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "WARNING:absl:The input size (78) is not a multiple of the number of attention heads (8)\n"
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    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "WARNING:absl:The input size (78) is not a multiple of the number of attention heads (8)\n"
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    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "WARNING:absl:The input size (78) is not a multiple of the number of attention heads (8)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[1m1/7\u001b[0m \u001b[32m━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m4s\u001b[0m 800ms/step"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step  \n"
     ]
    },
    {
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     "output_type": "stream",
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      "WARNING:absl:The input size (78) is not a multiple of the number of attention heads (8)\n"
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    {
     "name": "stderr",
     "output_type": "stream",
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      "WARNING:absl:The input size (78) is not a multiple of the number of attention heads (8)\n"
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    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "WARNING:absl:The input size (78) is not a multiple of the number of attention heads (8)\n"
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:The input size (78) is not a multiple of the number of attention heads (8)\n"
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    {
     "name": "stderr",
     "output_type": "stream",
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      "WARNING:absl:The input size (78) is not a multiple of the number of attention heads (8)\n"
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:The input size (78) is not a multiple of the number of attention heads (8)\n"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[1m1/7\u001b[0m \u001b[32m━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m4s\u001b[0m 787ms/step"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step  \n"
     ]
    }
   ],
   "source": [
    "import pickle\n",
    "\n",
    "from sklearn.metrics import (\n",
    "    accuracy_score,\n",
    "    recall_score,\n",
    "    precision_score,\n",
    "    confusion_matrix,\n",
    "    roc_auc_score,\n",
    "    roc_curve,\n",
    "    precision_recall_curve,\n",
    "    auc,\n",
    ")\n",
    "from utils.class_voter import combineProbas\n",
    "from utils.prepare_data import DeepLearningDataPreparer, normalizeData, patientsToNumpy, trainValTestPatients\n",
    "from constants import CATEGORICAL_MEASURES, TEMP_PATH\n",
    "import numpy as np\n",
    "from tensorflow.keras.callbacks import EarlyStopping\n",
    "\n",
    "\n",
    "accuracy_score_list = []\n",
    "precision_score_list = []\n",
    "recall_score_list = []\n",
    "auc_score_list = []\n",
    "specificity_score_list = []\n",
    "sensitivity_score_list = []\n",
    "auc_pr_list = []\n",
    "\n",
    "train_loss_list = []\n",
    "val_loss_list = []\n",
    "models = []\n",
    "for i, (trainValGenerator, testPatients) in enumerate(\n",
    "    trainValTestPatients(patients, splitSeed)\n",
    "):\n",
    "    models = []\n",
    "    probas = []\n",
    "    for ii, (trainPatients, valPatients) in enumerate(trainValGenerator):\n",
    "        preparer = DeepLearningDataPreparer(\n",
    "            hoursPerWindows=hoursPerWindow,\n",
    "            fromHour=0,\n",
    "            toHour=12,\n",
    "        )\n",
    "\n",
    "        cacheFile = TEMP_PATH / f\"dl_train_data/{i}_{ii}.pkl\"\n",
    "        cacheFile.parent.mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "        if cacheFile.exists():\n",
    "            (\n",
    "                (npTrainX, staticTrainX, trainY),\n",
    "                (npValX, staticValX, valY),\n",
    "                (npTestX, staticTestX, testY),\n",
    "            ) = pickle.loads(cacheFile.read_bytes())\n",
    "        else:\n",
    "            npTrainX, staticTrainX, trainY = preparer.fit_transform(trainPatients)\n",
    "            npValX, staticValX, valY = preparer.transform(valPatients)\n",
    "            npTestX, staticTestX, testY = preparer.transform(testPatients)\n",
    "            cacheFile.write_bytes(\n",
    "                pickle.dumps(\n",
    "                    (\n",
    "                        (npTrainX, staticTrainX, trainY),\n",
    "                        (npValX, staticValX, valY),\n",
    "                        (npTestX, staticTestX, testY),\n",
    "                    )\n",
    "                )\n",
    "            )\n",
    "\n",
    "        neg, pos = np.bincount(trainY)\n",
    "        weight0 = (1 / neg) * (len(trainY)) / 2.0\n",
    "        weight1 = (1 / pos) * (len(trainY)) / 2.0\n",
    "        weight = {0: weight0, 1: weight1}\n",
    "\n",
    "        early_stopping = EarlyStopping(\n",
    "            monitor=\"val_loss\", patience=250, restore_best_weights=True\n",
    "        )\n",
    "\n",
    "        # expand static data to be time steps as time series data\n",
    "        staticTrainX = np.expand_dims(staticTrainX, axis=1)\n",
    "        staticTrainX = np.repeat(staticTrainX, npTrainX.shape[1], axis=1)\n",
    "\n",
    "        staticTestX = np.expand_dims(staticTestX, axis=1)\n",
    "        staticTestX = np.repeat(staticTestX, npTestX.shape[1], axis=1)\n",
    "\n",
    "        staticValX = np.expand_dims(staticValX, axis=1)\n",
    "        staticValX = np.repeat(staticValX, npValX.shape[1], axis=1)\n",
    "        # append to npX\n",
    "        npTrainX = np.concatenate([npTrainX, staticTrainX], axis=2)\n",
    "        npTestX = np.concatenate([npTestX, staticTestX], axis=2)\n",
    "        npValX = np.concatenate([npValX, staticValX], axis=2)\n",
    "\n",
    "        model = createTransformerModel(npTrainX.shape[1], npTrainX.shape[2])\n",
    "        history = model.fit(\n",
    "            npTrainX,\n",
    "            np.array(trainY),\n",
    "            epochs=1000,\n",
    "            batch_size=32,\n",
    "            validation_data=(npValX, np.array(valY)),\n",
    "            class_weight=weight,\n",
    "            callbacks=[early_stopping],\n",
    "            verbose=0,\n",
    "        )\n",
    "        train_loss_list.append(history.history[\"loss\"])\n",
    "        val_loss_list.append(history.history[\"val_loss\"])\n",
    "        probas.append(model.predict(npTestX))\n",
    "        model.save(f\"result/transformer_only_model_{i}_{ii}.keras\")\n",
    "\n",
    "    # calculate final probas, auc, accuracy, precision, recal\n",
    "    finalYProbas = np.apply_along_axis(combineProbas, 0, np.array(probas))\n",
    "    finalYPred = np.where(finalYProbas > 0.5, 1, 0)\n",
    "    tn, fp, fn, tp = confusion_matrix(testY, finalYPred).ravel()\n",
    "    precision_vals, recall_vals, _ = precision_recall_curve(testY, finalYProbas)\n",
    "\n",
    "    accuracy_score_list.append(accuracy_score(testY, finalYPred))\n",
    "    specificity_score_list.append(tn / (tn + fp))\n",
    "    precision_score_list.append(precision_score(testY, finalYPred))\n",
    "    recall_score_list.append(recall_score(testY, finalYPred))\n",
    "    auc_score_list.append(roc_auc_score(testY, finalYProbas))\n",
    "    auc_pr_list.append(auc(recall_vals, precision_vals))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-30T11:32:28.668941Z",
     "iopub.status.busy": "2024-08-30T11:32:28.668775Z",
     "iopub.status.idle": "2024-08-30T11:32:28.670899Z",
     "shell.execute_reply": "2024-08-30T11:32:28.670737Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC: 0.8072732371104886 +- 0.01658864924513635 [0.8335949764521193, 0.7936507936507937, 0.8081324778914514, 0.8147216923877232, 0.7862662451703548]\n",
      "Accuracy: 0.7304087301587302 +- 0.03811980771841313 [0.7955555555555556, 0.7466666666666667, 0.6844444444444444, 0.7155555555555555, 0.7098214285714286]\n",
      "Specificity: 0.7128692572919578 +- 0.07416241077343866 [0.8163265306122449, 0.7891156462585034, 0.6506849315068494, 0.6643835616438356, 0.6438356164383562]\n",
      "Precision: 0.5941686818118826 +- 0.05494053029831608 [0.686046511627907, 0.6265060240963856, 0.5363636363636364, 0.5663716814159292, 0.5555555555555556]\n",
      "Recall: 0.7626744563453424 +- 0.057882624983912105 [0.7564102564102564, 0.6666666666666666, 0.7468354430379747, 0.810126582278481, 0.8333333333333334]\n",
      "AUC PR: 0.6856751281568695 +- 0.02599094704707963 [0.7291953378111595, 0.6695855509540594, 0.700366362346464, 0.6724182102266357, 0.6568101794460286]\n"
     ]
    }
   ],
   "source": [
    "print(f\"AUC: {np.mean(auc_score_list)} +- {np.std(auc_score_list)}\", auc_score_list)\n",
    "print(f\"Accuracy: {np.mean(accuracy_score_list)} +- {np.std(accuracy_score_list)}\", accuracy_score_list)\n",
    "print(f\"Specificity: {np.mean(specificity_score_list)} +- {np.std(specificity_score_list)}\", specificity_score_list)\n",
    "print(f\"Precision: {np.mean(precision_score_list)} +- {np.std(precision_score_list)}\", precision_score_list)\n",
    "print(f\"Recall: {np.mean(recall_score_list)} +- {np.std(recall_score_list)}\", recall_score_list)\n",
    "print(f\"AUC PR: {np.mean(auc_pr_list)} +- {np.std(auc_pr_list)}\", auc_pr_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-30T11:32:28.671576Z",
     "iopub.status.busy": "2024-08-30T11:32:28.671512Z",
     "iopub.status.idle": "2024-08-30T11:32:29.219157Z",
     "shell.execute_reply": "2024-08-30T11:32:29.218743Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.cm as cm\n",
    "\n",
    "\n",
    "plt.figure(figsize=(12, 6))\n",
    "colors = cm.viridis(np.linspace(0, 1, 5))\n",
    "for i, train_loss in enumerate(train_loss_list):\n",
    "    plt.plot(\n",
    "        range(1, len(train_loss) + 1),\n",
    "        train_loss,\n",
    "        label=f\"Fold {i+1} Training Loss\",\n",
    "        color=colors[i // 5],\n",
    "    )\n",
    "plt.title(\"Training Loss for Each Fold\")\n",
    "plt.xlabel(\"Epochs\")\n",
    "plt.ylabel(\"Loss\")\n",
    "plt.legend()\n",
    "plt.savefig(\"result/transformer_only_train_loss.svg\")\n",
    "plt.show()\n",
    "\n",
    "# Plot the validation loss for each fold\n",
    "plt.figure(figsize=(12, 6))\n",
    "for i, val_loss in enumerate(val_loss_list):\n",
    "    plt.plot(\n",
    "        range(1, len(val_loss) + 1),\n",
    "        val_loss,\n",
    "        label=f\"Fold {i+1} Validation Loss\",\n",
    "        color=colors[i // 5],\n",
    "    )\n",
    "plt.title(\"Validation Loss for Each Fold\")\n",
    "plt.xlabel(\"Epochs\")\n",
    "plt.ylabel(\"Loss\")\n",
    "plt.legend()\n",
    "plt.savefig(\"result/transformer_only_val_loss.svg\")\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}