ESM+Transformer_Flu.ipynb

{
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   "outputs": [
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/kaggle/input/h5-98cluster/H5_GisaidData.clustered.aligned.fasta\n"
     ]
    }
   ],
   "source": [
    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
    "# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
    "# For example, here's several helpful packages to load\n",
    "\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "\n",
    "# Input data files are available in the read-only \"../input/\" directory\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
    "\n",
    "import os\n",
    "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
    "    for filename in filenames:\n",
    "        print(os.path.join(dirname, filename))\n",
    "\n",
    "# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n",
    "# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
   ]
  },
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting esm\r\n",
      "  Downloading esm-3.0.7.post1-py3-none-any.whl.metadata (9.4 kB)\r\n",
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      "Collecting biotite==0.41.2 (from esm)\r\n",
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      "\u001b[?25hInstalling collected packages: msgpack-numpy, einops, biopython, biotite, torchtext, esm\r\n",
      "Successfully installed biopython-1.84 biotite-0.41.2 einops-0.8.0 esm-3.0.7.post1 msgpack-numpy-0.4.8 torchtext-0.18.0\r\n"
     ]
    }
   ],
   "source": [
    "!pip install esm"
   ]
  },
  {
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   "id": "a09070d9",
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   "outputs": [],
   "source": [
    "import Bio.SeqIO\n",
    "import seaborn as sns\n",
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "from datetime import date\n",
    "from Bio.Align import PairwiseAligner\n",
    "from Bio.Data import CodonTable\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.feature_selection import SelectKBest,f_classif"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5d5f3d57",
   "metadata": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from huggingface_hub import login\n",
    "from esm.models.esm3 import ESM3\n",
    "from esm.sdk.api import ESM3InferenceClient, ESMProtein, GenerationConfig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a01fb6b2",
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    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The token has not been saved to the git credentials helper. Pass `add_to_git_credential=True` in this function directly or `--add-to-git-credential` if using via `huggingface-cli` if you want to set the git credential as well.\n",
      "Token is valid (permission: read).\n",
      "Your token has been saved to /root/.cache/huggingface/token\n",
      "Login successful\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/transformers/tokenization_utils_base.py:1617: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be deprecated in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884\n",
      "  warnings.warn(\n"
     ]
    },
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/esm/pretrained.py:68: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  state_dict = torch.load(\n"
     ]
    }
   ],
   "source": [
    "from kaggle_secrets import UserSecretsClient\n",
    "user_secrets = UserSecretsClient()\n",
    "secret_value_0 = user_secrets.get_secret(\"ESM\")\n",
    "login(token=secret_value_0)\n",
    "model: ESM3InferenceClient = ESM3.from_pretrained(\"esm3-open\").to(\"cuda\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4c4289a2",
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total sequences loaded: 591\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "\n",
    "# Path to your FASTA file\n",
    "path = '/kaggle/input/h5-98cluster/H5_GisaidData.clustered.aligned.fasta'\n",
    "\n",
    "# Initialize an empty list to store sequences\n",
    "sequences = []\n",
    "\n",
    "# Open the FASTA file and read sequences\n",
    "with open(path, 'r') as file:\n",
    "    sequence = ''\n",
    "    for line in file:\n",
    "        line = line.strip()\n",
    "        if line.startswith('>'):\n",
    "            if sequence:\n",
    "                sequences.append(sequence)\n",
    "                sequence = ''\n",
    "        else:\n",
    "            sequence += line\n",
    "    if sequence:\n",
    "        sequences.append(sequence)\n",
    "\n",
    "print(f\"Total sequences loaded: {len(sequences)}\")\n",
    "\n"
   ]
  },
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       "vocab.txt:   0%|          | 0.00/93.0 [00:00<?, ?B/s]"
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    {
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      "text/plain": [
       "special_tokens_map.json:   0%|          | 0.00/125 [00:00<?, ?B/s]"
      ]
     },
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/transformers/tokenization_utils_base.py:1617: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be deprecated in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884\n",
      "  warnings.warn(\n"
     ]
    },
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       "config.json:   0%|          | 0.00/775 [00:00<?, ?B/s]"
      ]
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    },
    {
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      "text/plain": [
       "model.safetensors:   0%|          | 0.00/31.4M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of EsmModel were not initialized from the model checkpoint at facebook/esm2_t6_8M_UR50D and are newly initialized: ['esm.pooler.dense.bias', 'esm.pooler.dense.weight']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
     ]
    }
   ],
   "source": [
    "from transformers import EsmTokenizer, EsmModel\n",
    "import torch\n",
    "\n",
    "tokenizer = EsmTokenizer.from_pretrained(\"facebook/esm2_t6_8M_UR50D\")\n",
    "model = EsmModel.from_pretrained(\"facebook/esm2_t6_8M_UR50D\")"
   ]
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   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Asking to truncate to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no truncation.\n"
     ]
    }
   ],
   "source": [
    "# Initialize dictionary to store embeddings\n",
    "embeddings = {}\n",
    "\n",
    "# Loop over sequences and compute embeddings\n",
    "for idx, seq in enumerate(sequences):\n",
    "    # Tokenize the sequence\n",
    "    inputs = tokenizer(seq, return_tensors=\"pt\", padding=True, truncation=True)\n",
    "    \n",
    "    # Get model outputs\n",
    "    outputs = model(**inputs)\n",
    "    \n",
    "    # Extract last hidden states and compute mean across tokens\n",
    "    last_hidden_states = outputs.last_hidden_state\n",
    "    x = last_hidden_states.detach()\n",
    "    embedding = x.mean(axis=1)  # Average embedding for the sequence\n",
    "    \n",
    "    # Store embedding in dictionary with unique index\n",
    "    embeddings[f'idx_{idx}'] = embedding\n",
    "\n",
    "# embeddings dictionary now contains mean embeddings for each sequence"
   ]
  },
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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: \"functional_1\"</span>\n",
       "</pre>\n"
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       "\u001b[1mModel: \"functional_1\"\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>┃<span style=\"font-weight: bold\"> Connected to      </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩\n",
       "│ input_layer         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">320</span>)    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ -                 │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)        │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ not_equal           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">320</span>)    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ input_layer[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">NotEqual</span>)          │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ masking (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Masking</span>)   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">320</span>)    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ input_layer[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ any (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Any</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)         │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ not_equal[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ positional_encoding │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">320</span>)    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ masking[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],    │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">PositionalEncodin…</span> │                   │            │ any[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]         │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ multi_head_attenti… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">320</span>)    │  <span style=\"color: #00af00; text-decoration-color: #00af00\">3,284,800</span> │ positional_encod… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MultiHeadAttentio…</span> │                   │            │ positional_encod… │\n",
       "│                     │                   │            │ any[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],        │\n",
       "│                     │                   │            │ any[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]         │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ dropout_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">320</span>)    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ multi_head_atten… │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ add (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Add</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">320</span>)    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ positional_encod… │\n",
       "│                     │                   │            │ dropout_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ layer_normalization │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">320</span>)    │        <span style=\"color: #00af00; text-decoration-color: #00af00\">640</span> │ add[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]         │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LayerNormalizatio…</span> │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)    │     <span style=\"color: #00af00; text-decoration-color: #00af00\">82,176</span> │ layer_normalizat… │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ dense_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">320</span>)    │     <span style=\"color: #00af00; text-decoration-color: #00af00\">82,240</span> │ dense[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]       │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ dropout_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">320</span>)    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ dense_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]     │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ add_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Add</span>)         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">320</span>)    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ layer_normalizat… │\n",
       "│                     │                   │            │ dropout_2[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ layer_normalizatio… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">320</span>)    │        <span style=\"color: #00af00; text-decoration-color: #00af00\">640</span> │ add_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]       │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LayerNormalizatio…</span> │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ multi_head_attenti… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">320</span>)    │  <span style=\"color: #00af00; text-decoration-color: #00af00\">3,284,800</span> │ layer_normalizat… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MultiHeadAttentio…</span> │                   │            │ layer_normalizat… │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ dropout_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">320</span>)    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ multi_head_atten… │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ add_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Add</span>)         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">320</span>)    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ layer_normalizat… │\n",
       "│                     │                   │            │ dropout_4[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ layer_normalizatio… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">320</span>)    │        <span style=\"color: #00af00; text-decoration-color: #00af00\">640</span> │ add_2[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]       │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LayerNormalizatio…</span> │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ dense_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)    │     <span style=\"color: #00af00; text-decoration-color: #00af00\">82,176</span> │ layer_normalizat… │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\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\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">320</span>)    │     <span style=\"color: #00af00; text-decoration-color: #00af00\">82,240</span> │ dense_2[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]     │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ dropout_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">320</span>)    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ dense_3[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]     │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ add_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Add</span>)         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">320</span>)    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ layer_normalizat… │\n",
       "│                     │                   │            │ dropout_5[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ layer_normalizatio… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">320</span>)    │        <span style=\"color: #00af00; text-decoration-color: #00af00\">640</span> │ add_3[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]       │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LayerNormalizatio…</span> │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\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\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">320</span>)    │    <span style=\"color: #00af00; text-decoration-color: #00af00\">102,720</span> │ layer_normalizat… │\n",
       "└─────────────────────┴───────────────────┴────────────┴───────────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)       \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape     \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m   Param #\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mConnected to     \u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩\n",
       "│ input_layer         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m320\u001b[0m)    │          \u001b[38;5;34m0\u001b[0m │ -                 │\n",
       "│ (\u001b[38;5;33mInputLayer\u001b[0m)        │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ not_equal           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m320\u001b[0m)    │          \u001b[38;5;34m0\u001b[0m │ input_layer[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
       "│ (\u001b[38;5;33mNotEqual\u001b[0m)          │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ masking (\u001b[38;5;33mMasking\u001b[0m)   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m320\u001b[0m)    │          \u001b[38;5;34m0\u001b[0m │ input_layer[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ any (\u001b[38;5;33mAny\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)         │          \u001b[38;5;34m0\u001b[0m │ not_equal[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ positional_encoding │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m320\u001b[0m)    │          \u001b[38;5;34m0\u001b[0m │ masking[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],    │\n",
       "│ (\u001b[38;5;33mPositionalEncodin…\u001b[0m │                   │            │ any[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]         │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ multi_head_attenti… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m320\u001b[0m)    │  \u001b[38;5;34m3,284,800\u001b[0m │ positional_encod… │\n",
       "│ (\u001b[38;5;33mMultiHeadAttentio…\u001b[0m │                   │            │ positional_encod… │\n",
       "│                     │                   │            │ any[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],        │\n",
       "│                     │                   │            │ any[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]         │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ dropout_1 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m320\u001b[0m)    │          \u001b[38;5;34m0\u001b[0m │ multi_head_atten… │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ add (\u001b[38;5;33mAdd\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m320\u001b[0m)    │          \u001b[38;5;34m0\u001b[0m │ positional_encod… │\n",
       "│                     │                   │            │ dropout_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ layer_normalization │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m320\u001b[0m)    │        \u001b[38;5;34m640\u001b[0m │ add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]         │\n",
       "│ (\u001b[38;5;33mLayerNormalizatio…\u001b[0m │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ dense (\u001b[38;5;33mDense\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m256\u001b[0m)    │     \u001b[38;5;34m82,176\u001b[0m │ layer_normalizat… │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ dense_1 (\u001b[38;5;33mDense\u001b[0m)     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m320\u001b[0m)    │     \u001b[38;5;34m82,240\u001b[0m │ dense[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]       │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ dropout_2 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m320\u001b[0m)    │          \u001b[38;5;34m0\u001b[0m │ dense_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]     │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ add_1 (\u001b[38;5;33mAdd\u001b[0m)         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m320\u001b[0m)    │          \u001b[38;5;34m0\u001b[0m │ layer_normalizat… │\n",
       "│                     │                   │            │ dropout_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ layer_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m320\u001b[0m)    │        \u001b[38;5;34m640\u001b[0m │ add_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]       │\n",
       "│ (\u001b[38;5;33mLayerNormalizatio…\u001b[0m │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ multi_head_attenti… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m320\u001b[0m)    │  \u001b[38;5;34m3,284,800\u001b[0m │ layer_normalizat… │\n",
       "│ (\u001b[38;5;33mMultiHeadAttentio…\u001b[0m │                   │            │ layer_normalizat… │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ dropout_4 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m320\u001b[0m)    │          \u001b[38;5;34m0\u001b[0m │ multi_head_atten… │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ add_2 (\u001b[38;5;33mAdd\u001b[0m)         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m320\u001b[0m)    │          \u001b[38;5;34m0\u001b[0m │ layer_normalizat… │\n",
       "│                     │                   │            │ dropout_4[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ layer_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m320\u001b[0m)    │        \u001b[38;5;34m640\u001b[0m │ add_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]       │\n",
       "│ (\u001b[38;5;33mLayerNormalizatio…\u001b[0m │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ dense_2 (\u001b[38;5;33mDense\u001b[0m)     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m256\u001b[0m)    │     \u001b[38;5;34m82,176\u001b[0m │ layer_normalizat… │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ dense_3 (\u001b[38;5;33mDense\u001b[0m)     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m320\u001b[0m)    │     \u001b[38;5;34m82,240\u001b[0m │ dense_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]     │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ dropout_5 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m320\u001b[0m)    │          \u001b[38;5;34m0\u001b[0m │ dense_3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]     │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ add_3 (\u001b[38;5;33mAdd\u001b[0m)         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m320\u001b[0m)    │          \u001b[38;5;34m0\u001b[0m │ layer_normalizat… │\n",
       "│                     │                   │            │ dropout_5[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ layer_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m320\u001b[0m)    │        \u001b[38;5;34m640\u001b[0m │ add_3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]       │\n",
       "│ (\u001b[38;5;33mLayerNormalizatio…\u001b[0m │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ dense_4 (\u001b[38;5;33mDense\u001b[0m)     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m320\u001b[0m)    │    \u001b[38;5;34m102,720\u001b[0m │ layer_normalizat… │\n",
       "└─────────────────────┴───────────────────┴────────────┴───────────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">7,003,712</span> (26.72 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m7,003,712\u001b[0m (26.72 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">7,003,712</span> (26.72 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m7,003,712\u001b[0m (26.72 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/keras/src/layers/layer.py:877: UserWarning: Layer 'query' (of type EinsumDense) was passed an input with a mask attached to it. However, this layer does not support masking and will therefore destroy the mask information. Downstream layers will not see the mask.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/keras/src/layers/layer.py:877: UserWarning: Layer 'key' (of type EinsumDense) was passed an input with a mask attached to it. However, this layer does not support masking and will therefore destroy the mask information. Downstream layers will not see the mask.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/keras/src/layers/layer.py:877: UserWarning: Layer 'value' (of type EinsumDense) was passed an input with a mask attached to it. However, this layer does not support masking and will therefore destroy the mask information. Downstream layers will not see the mask.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/keras/src/ops/nn.py:545: UserWarning: You are using a softmax over axis 3 of a tensor of shape (None, 8, 1, 1). This axis has size 1. The softmax operation will always return the value 1, which is likely not what you intended. Did you mean to use a sigmoid instead?\n",
      "  warnings.warn(\n",
      "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
      "I0000 00:00:1731556781.694320      82 service.cc:145] XLA service 0x7fbb48001630 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n",
      "I0000 00:00:1731556781.694382      82 service.cc:153]   StreamExecutor device (0): Tesla P100-PCIE-16GB, Compute Capability 6.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m13/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.5679 - mae: 0.5272  "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "I0000 00:00:1731556791.333568      82 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[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m30s\u001b[0m 966ms/step - loss: 0.5144 - mae: 0.4953 - val_loss: 0.0492 - val_mae: 0.1229\n",
      "Epoch 2/64\n",
      "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0972 - mae: 0.2175 - val_loss: 0.0336 - val_mae: 0.0762\n",
      "Epoch 3/64\n",
      "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0811 - mae: 0.1939 - val_loss: 0.0323 - val_mae: 0.0685\n",
      "Epoch 4/64\n",
      "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0731 - mae: 0.1804 - val_loss: 0.0321 - val_mae: 0.0723\n",
      "Epoch 5/64\n",
      "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0653 - mae: 0.1672 - val_loss: 0.0316 - val_mae: 0.0700\n",
      "Epoch 6/64\n",
      "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0598 - mae: 0.1575 - val_loss: 0.0303 - val_mae: 0.0662\n",
      "Epoch 7/64\n",
      "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0566 - mae: 0.1514 - val_loss: 0.0301 - val_mae: 0.0666\n",
      "Epoch 8/64\n",
      "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0546 - mae: 0.1470 - val_loss: 0.0301 - val_mae: 0.0649\n",
      "Epoch 9/64\n",
      "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0516 - mae: 0.1409 - val_loss: 0.0300 - val_mae: 0.0662\n",
      "Epoch 10/64\n",
      "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0490 - mae: 0.1357 - val_loss: 0.0285 - val_mae: 0.0626\n",
      "Epoch 11/64\n",
      "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0462 - mae: 0.1293 - val_loss: 0.0286 - val_mae: 0.0647\n",
      "Epoch 12/64\n",
      "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0434 - mae: 0.1230 - val_loss: 0.0282 - val_mae: 0.0602\n",
      "Epoch 13/64\n",
      "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0400 - mae: 0.1145 - val_loss: 0.0280 - val_mae: 0.0650\n",
      "Epoch 14/64\n",
      "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0386 - mae: 0.1123 - val_loss: 0.0271 - val_mae: 0.0625\n",
      "Epoch 15/64\n",
      "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0361 - mae: 0.1053 - val_loss: 0.0275 - val_mae: 0.0647\n",
      "Epoch 16/64\n",
      "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0345 - mae: 0.1001 - val_loss: 0.0253 - val_mae: 0.0569\n",
      "Epoch 17/64\n",
      "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0317 - mae: 0.0922 - val_loss: 0.0225 - val_mae: 0.0479\n",
      "Epoch 18/64\n",
      "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0286 - mae: 0.0828 - val_loss: 0.0218 - val_mae: 0.0482\n",
      "Epoch 19/64\n",
      "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0276 - mae: 0.0789 - val_loss: 0.0201 - val_mae: 0.0393\n",
      "Epoch 20/64\n",
      "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0252 - mae: 0.0701 - val_loss: 0.0198 - val_mae: 0.0392\n",
      "Epoch 21/64\n",
      "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0237 - mae: 0.0638 - val_loss: 0.0190 - val_mae: 0.0335\n",
      "Epoch 22/64\n",
      "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0225 - mae: 0.0583 - val_loss: 0.0184 - val_mae: 0.0295\n",
      "Epoch 23/64\n",
      "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0218 - mae: 0.0555 - val_loss: 0.0182 - val_mae: 0.0299\n",
      "Epoch 24/64\n",
      "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0210 - mae: 0.0519 - val_loss: 0.0180 - val_mae: 0.0282\n",
      "Epoch 25/64\n",
      "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0204 - mae: 0.0497 - val_loss: 0.0176 - val_mae: 0.0260\n",
      "Epoch 26/64\n",
      "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0195 - mae: 0.0445 - val_loss: 0.0175 - val_mae: 0.0267\n",
      "Epoch 27/64\n",
      "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0195 - mae: 0.0445 - val_loss: 0.0174 - val_mae: 0.0286\n",
      "Epoch 28/64\n",
      "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0193 - mae: 0.0448 - val_loss: 0.0170 - val_mae: 0.0240\n",
      "Epoch 29/64\n",
      "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0184 - mae: 0.0399 - val_loss: 0.0171 - val_mae: 0.0282\n",
      "Epoch 30/64\n",
      "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0184 - mae: 0.0409 - val_loss: 0.0170 - val_mae: 0.0282\n",
      "Epoch 31/64\n",
      "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0180 - mae: 0.0389 - val_loss: 0.0171 - val_mae: 0.0308\n",
      "Epoch 32/64\n",
      "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0176 - mae: 0.0374 - val_loss: 0.0163 - val_mae: 0.0229\n",
      "Epoch 33/64\n",
      "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0174 - mae: 0.0364 - val_loss: 0.0161 - val_mae: 0.0223\n",
      "Epoch 34/64\n",
      "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0169 - mae: 0.0333 - val_loss: 0.0162 - val_mae: 0.0276\n",
      "Epoch 35/64\n",
      "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0168 - mae: 0.0344 - val_loss: 0.0158 - val_mae: 0.0223\n",
      "Epoch 36/64\n",
      "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0164 - mae: 0.0319 - val_loss: 0.0156 - val_mae: 0.0230\n",
      "Epoch 37/64\n",
      "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0165 - mae: 0.0329 - val_loss: 0.0154 - val_mae: 0.0221\n",
      "Epoch 38/64\n",
      "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0160 - mae: 0.0312 - val_loss: 0.0155 - val_mae: 0.0249\n",
      "Epoch 39/64\n",
      "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0160 - mae: 0.0318 - val_loss: 0.0151 - val_mae: 0.0223\n",
      "Epoch 40/64\n",
      "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0157 - mae: 0.0311 - val_loss: 0.0150 - val_mae: 0.0224\n",
      "Epoch 41/64\n",
      "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0154 - mae: 0.0290 - val_loss: 0.0150 - val_mae: 0.0234\n",
      "Epoch 42/64\n",
      "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0154 - mae: 0.0307 - val_loss: 0.0149 - val_mae: 0.0245\n",
      "Epoch 43/64\n",
      "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0151 - mae: 0.0294 - val_loss: 0.0145 - val_mae: 0.0202\n",
      "Epoch 44/64\n",
      "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.0149 - mae: 0.0280 - val_loss: 0.0145 - val_mae: 0.0232\n",
      "Epoch 45/64\n",
      "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0146 - mae: 0.0268 - val_loss: 0.0142 - val_mae: 0.0213\n",
      "Epoch 46/64\n",
      "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0146 - mae: 0.0281 - val_loss: 0.0142 - val_mae: 0.0228\n",
      "Epoch 47/64\n",
      "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0145 - mae: 0.0284 - val_loss: 0.0140 - val_mae: 0.0207\n",
      "Epoch 48/64\n",
      "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0142 - mae: 0.0264 - val_loss: 0.0139 - val_mae: 0.0216\n",
      "Epoch 49/64\n",
      "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0140 - mae: 0.0261 - val_loss: 0.0138 - val_mae: 0.0229\n",
      "Epoch 50/64\n",
      "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0140 - mae: 0.0276 - val_loss: 0.0135 - val_mae: 0.0207\n",
      "Epoch 51/64\n",
      "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0139 - mae: 0.0274 - val_loss: 0.0135 - val_mae: 0.0222\n",
      "Epoch 52/64\n",
      "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0135 - mae: 0.0253 - val_loss: 0.0132 - val_mae: 0.0196\n",
      "Epoch 53/64\n",
      "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0134 - mae: 0.0248 - val_loss: 0.0131 - val_mae: 0.0198\n",
      "Epoch 54/64\n",
      "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0134 - mae: 0.0257 - val_loss: 0.0130 - val_mae: 0.0202\n",
      "Epoch 55/64\n",
      "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0132 - mae: 0.0256 - val_loss: 0.0129 - val_mae: 0.0210\n",
      "Epoch 56/64\n",
      "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0132 - mae: 0.0263 - val_loss: 0.0130 - val_mae: 0.0249\n",
      "Epoch 57/64\n",
      "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0129 - mae: 0.0259 - val_loss: 0.0127 - val_mae: 0.0223\n",
      "Epoch 58/64\n",
      "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0129 - mae: 0.0260 - val_loss: 0.0127 - val_mae: 0.0247\n",
      "Epoch 59/64\n",
      "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0127 - mae: 0.0255 - val_loss: 0.0126 - val_mae: 0.0239\n",
      "Epoch 60/64\n",
      "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0126 - mae: 0.0265 - val_loss: 0.0123 - val_mae: 0.0208\n",
      "Epoch 61/64\n",
      "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0126 - mae: 0.0265 - val_loss: 0.0124 - val_mae: 0.0239\n",
      "Epoch 62/64\n",
      "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0122 - mae: 0.0243 - val_loss: 0.0120 - val_mae: 0.0190\n",
      "Epoch 63/64\n",
      "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0121 - mae: 0.0239 - val_loss: 0.0118 - val_mae: 0.0194\n",
      "Epoch 64/64\n",
      "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0119 - mae: 0.0230 - val_loss: 0.0117 - val_mae: 0.0186\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.0118 - mae: 0.0193\n",
      "Test Loss (MSE): 0.011666, Test MAE: 0.018568\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/keras/src/ops/nn.py:545: UserWarning: You are using a softmax over axis 3 of a tensor of shape (32, 8, 1, 1). This axis has size 1. The softmax operation will always return the value 1, which is likely not what you intended. Did you mean to use a sigmoid instead?\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 195ms/step\n",
      "Test Reconstruction Loss: 0.000902\n"
     ]
    }
   ],
   "source": [
    "# Import necessary libraries\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras import layers, models\n",
    "from tensorflow.keras.layers import Input, Dense, LayerNormalization, Dropout\n",
    "from tensorflow.keras.layers import MultiHeadAttention, Layer\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.regularizers import l2  # Import L2 regularizer\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Assume embeddings is a dictionary: {sequence_id: embedding_array}\n",
    "# Each embedding_array has shape (sequence_length, embedding_dim)\n",
    "\n",
    "# Extract embeddings and pad sequences to the same length\n",
    "embedding_list = []\n",
    "sequence_ids = []\n",
    "max_seq_length = 0\n",
    "\n",
    "# Find the maximum sequence length\n",
    "for seq_id, emb in embeddings.items():\n",
    "    if emb.shape[0] > max_seq_length:\n",
    "        max_seq_length = emb.shape[0]\n",
    "\n",
    "# Pad embeddings to the maximum sequence length\n",
    "for seq_id, emb in embeddings.items():\n",
    "    seq_length, embedding_dim = emb.shape\n",
    "    # Pad the sequence to max_seq_length\n",
    "    pad_length = max_seq_length - seq_length\n",
    "    if pad_length > 0:\n",
    "        # Pad with zeros\n",
    "        emb_padded = np.pad(emb, ((0, pad_length), (0, 0)), mode='constant', constant_values=0)\n",
    "    else:\n",
    "        emb_padded = emb\n",
    "    embedding_list.append(emb_padded)\n",
    "    sequence_ids.append(seq_id)\n",
    "\n",
    "embedding_array = np.stack(embedding_list)  # Shape: (num_sequences, max_seq_length, embedding_dim)\n",
    "\n",
    "# Split the data into training and test sets\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Create sequence indices\n",
    "sequence_indices = np.arange(embedding_array.shape[0])\n",
    "\n",
    "# Perform the train/test split\n",
    "train_indices, test_indices = train_test_split(\n",
    "    sequence_indices,\n",
    "    test_size=0.2,       # 20% of data for testing\n",
    "    random_state=42,     # For reproducibility\n",
    "    shuffle=True\n",
    ")\n",
    "\n",
    "# Split the embeddings\n",
    "embedding_array_train = embedding_array[train_indices]\n",
    "embedding_array_test = embedding_array[test_indices]\n",
    "\n",
    "# Transformer parameters\n",
    "embedding_dim = embedding_array.shape[2]\n",
    "num_heads = 8\n",
    "ff_dim = 256  # Feed-forward layer dimension\n",
    "dropout_rate = 0.1\n",
    "num_layers = 2  # Number of transformer blocks\n",
    "l2_reg = 1e-5  # L2 regularization factor\n",
    "\n",
    "# Input layer\n",
    "inputs = Input(shape=(max_seq_length, embedding_dim))\n",
    "\n",
    "# Add a Masking layer\n",
    "x = layers.Masking(mask_value=0.0)(inputs)\n",
    "\n",
    "# Positional Encoding Layer\n",
    "class PositionalEncoding(Layer):\n",
    "    def __init__(self, max_seq_len, embed_dim, **kwargs):\n",
    "        super(PositionalEncoding, self).__init__(**kwargs)\n",
    "        self.max_seq_len = max_seq_len\n",
    "        self.embed_dim = embed_dim\n",
    "        self.pos_encoding = self.positional_encoding(max_seq_len, embed_dim)\n",
    "        self.supports_masking = True  # Ensure masking is supported\n",
    "\n",
    "    def get_angles(self, pos, i, embed_dim):\n",
    "        angle_rates = 1 / np.power(10000, (2 * (i // 2)) / np.float32(embed_dim))\n",
    "        return pos * angle_rates\n",
    "\n",
    "    def positional_encoding(self, max_seq_len, embed_dim):\n",
    "        angle_rads = self.get_angles(\n",
    "            np.arange(max_seq_len)[:, np.newaxis],\n",
    "            np.arange(embed_dim)[np.newaxis, :],\n",
    "            embed_dim\n",
    "        )\n",
    "\n",
    "        # Apply sin to even indices\n",
    "        sines = np.sin(angle_rads[:, 0::2])\n",
    "\n",
    "        # Apply cos to odd indices\n",
    "        cosines = np.cos(angle_rads[:, 1::2])\n",
    "\n",
    "        pos_encoding = np.zeros(angle_rads.shape)\n",
    "        pos_encoding[:, 0::2] = sines\n",
    "        pos_encoding[:, 1::2] = cosines\n",
    "\n",
    "        pos_encoding = pos_encoding[np.newaxis, ...]\n",
    "\n",
    "        return tf.cast(pos_encoding, dtype=tf.float32)\n",
    "\n",
    "    def call(self, inputs, mask=None):\n",
    "        return inputs + self.pos_encoding[:, :tf.shape(inputs)[1], :]\n",
    "\n",
    "    def compute_mask(self, inputs, mask=None):\n",
    "        return mask\n",
    "\n",
    "# Add positional encoding\n",
    "x = PositionalEncoding(max_seq_length, embedding_dim)(x)\n",
    "\n",
    "# Transformer Encoder blocks\n",
    "for _ in range(num_layers):\n",
    "    # Multi-head attention with L2 regularization\n",
    "    attn_output = MultiHeadAttention(\n",
    "        num_heads=num_heads,\n",
    "        key_dim=embedding_dim,\n",
    "        kernel_regularizer=l2(l2_reg),\n",
    "        bias_regularizer=l2(l2_reg),\n",
    "    )(x, x, attention_mask=None)\n",
    "    attn_output = Dropout(dropout_rate)(attn_output)\n",
    "    out1 = LayerNormalization(epsilon=1e-6)(x + attn_output)\n",
    "\n",
    "    # Feed-forward network with L2 regularization\n",
    "    ffn_output = Dense(\n",
    "        ff_dim,\n",
    "        activation='relu',\n",
    "        kernel_regularizer=l2(l2_reg),\n",
    "        bias_regularizer=l2(l2_reg)\n",
    "    )(out1)\n",
    "    ffn_output = Dense(\n",
    "        embedding_dim,\n",
    "        kernel_regularizer=l2(l2_reg),\n",
    "        bias_regularizer=l2(l2_reg)\n",
    "    )(ffn_output)\n",
    "    ffn_output = Dropout(dropout_rate)(ffn_output)\n",
    "    x = LayerNormalization(epsilon=1e-6)(out1 + ffn_output)\n",
    "\n",
    "# Output layer with L2 regularization\n",
    "outputs = Dense(\n",
    "    embedding_dim,\n",
    "    kernel_regularizer=l2(l2_reg),\n",
    "    bias_regularizer=l2(l2_reg)\n",
    ")(x)\n",
    "\n",
    "# Define the model\n",
    "model = Model(inputs=inputs, outputs=outputs)\n",
    "\n",
    "# Print the model summary\n",
    "model.summary()\n",
    "\n",
    "# Compile the model with additional metrics\n",
    "model.compile(optimizer='adam', loss='mse', metrics=['mae'])\n",
    "\n",
    "# Training parameters\n",
    "epochs = 64\n",
    "batch_size = 32\n",
    "\n",
    "# Prepare datasets\n",
    "# Training dataset\n",
    "dataset_train = tf.data.Dataset.from_tensor_slices((embedding_array_train, embedding_array_train))\n",
    "dataset_train = dataset_train.shuffle(buffer_size=1024).batch(batch_size)\n",
    "\n",
    "# Validation dataset\n",
    "dataset_val = tf.data.Dataset.from_tensor_slices((embedding_array_test, embedding_array_test))\n",
    "dataset_val = dataset_val.batch(batch_size)\n",
    "\n",
    "# Train the model with validation data\n",
    "history = model.fit(\n",
    "    dataset_train,\n",
    "    epochs=epochs,\n",
    "    validation_data=dataset_val\n",
    ")\n",
    "\n",
    "# Evaluate the model on the test data\n",
    "test_loss, test_mae = model.evaluate(dataset_val)\n",
    "print(f'Test Loss (MSE): {test_loss:.6f}, Test MAE: {test_mae:.6f}')\n",
    "\n",
    "# Compute reconstruction loss on test data\n",
    "reconstructed_embeddings_test = model.predict(embedding_array_test)\n",
    "reconstruction_loss_test = np.mean((embedding_array_test - reconstructed_embeddings_test) ** 2)\n",
    "print(f'Test Reconstruction Loss: {reconstruction_loss_test:.6f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "052b1258",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-14T04:00:11.297601Z",
     "iopub.status.busy": "2024-11-14T04:00:11.296833Z",
     "iopub.status.idle": "2024-11-14T04:00:11.619092Z",
     "shell.execute_reply": "2024-11-14T04:00:11.618180Z"
    },
    "papermill": {
     "duration": 0.359346,
     "end_time": "2024-11-14T04:00:11.621292",
     "exception": false,
     "start_time": "2024-11-14T04:00:11.261946",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Access loss and validation loss from history\n",
    "loss = history.history['loss']\n",
    "val_loss = history.history['val_loss']\n",
    "epochs_range = range(1, len(loss) + 1)\n",
    "\n",
    "# Plot Loss over epochs\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.plot(epochs_range, loss, 'b-', label='Training Loss')\n",
    "plt.plot(epochs_range, val_loss, 'r-', label='Validation Loss')\n",
    "plt.title('Training and Validation Loss over Epochs')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss (MSE)')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kaggle": {
   "accelerator": "gpu",
   "dataSources": [
    {
     "datasetId": 6042372,
     "sourceId": 9847818,
     "sourceType": "datasetVersion"
    }
   ],
   "dockerImageVersionId": 30787,
   "isGpuEnabled": true,
   "isInternetEnabled": true,
   "language": "python",
   "sourceType": "notebook"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.10.14"
  },
  "papermill": {
   "default_parameters": {},
   "duration": 293.813826,
   "end_time": "2024-11-14T04:00:14.721678",
   "environment_variables": {},
   "exception": null,
   "input_path": "__notebook__.ipynb",
   "output_path": "__notebook__.ipynb",
   "parameters": {},
   "start_time": "2024-11-14T03:55:20.907852",
   "version": "2.6.0"
  },
  "widgets": {
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