tx · 2Mz8tbuiDBMobXfEPcNFhTBUL5fefaAc2nWNcrKZaF1L
3NAAoJ554QsZfqE8W8Rg8LsJb79d5b1pDat: -0.00500000 Waves
2025.03.06 16:41 [3532229] invoke 3NAAoJ554QsZfqE8W8Rg8LsJb79d5b1pDat > 3N9tKixzqTYWnEXQxrDQ5pBTGvQd6sFsvmV commitTask()
3N9tKixzqTYWnEXQxrDQ5pBTGvQd6sFsvmV: checked_out_by_Ct2djqZsAz77Ur5Y9pMbgrJR2xn6hDprcsHUYgLfsdkY_chatgpt_6e6wn4Jd9oTMKve4zLbNtye51wt7E6MTDEYT1gkLZQSy_AqqtiUWzxuW2sGQZiUBdYgDuY9J9GaL327FdWiEuh6qc: true -> null
3N9tKixzqTYWnEXQxrDQ5pBTGvQd6sFsvmV: 6e6wn4Jd9oTMKve4zLbNtye51wt7E6MTDEYT1gkLZQSy_AqqtiUWzxuW2sGQZiUBdYgDuY9J9GaL327FdWiEuh6qc_commit_timestamp_chatgpt: 1741268514625
3N9tKixzqTYWnEXQxrDQ5pBTGvQd6sFsvmV: 6e6wn4Jd9oTMKve4zLbNtye51wt7E6MTDEYT1gkLZQSy_AqqtiUWzxuW2sGQZiUBdYgDuY9J9GaL327FdWiEuh6qc_commit_height_chatgpt: 3532229
3N9tKixzqTYWnEXQxrDQ5pBTGvQd6sFsvmV: 6e6wn4Jd9oTMKve4zLbNtye51wt7E6MTDEYT1gkLZQSy_AqqtiUWzxuW2sGQZiUBdYgDuY9J9GaL327FdWiEuh6qc_result_chatgpt: "Long short-term memory (LSTM) is a type of recurrent neural network architecture developed to address the vanishing gradient problem in traditional RNNs, allowing for capturing long-term dependencies in sequential data. LSTMs utilize a system of gates to regulate the flow of information, enabling better retention and utilization of information over time in the network's memory cells."
3N9tKixzqTYWnEXQxrDQ5pBTGvQd6sFsvmV: 6e6wn4Jd9oTMKve4zLbNtye51wt7E6MTDEYT1gkLZQSy_AqqtiUWzxuW2sGQZiUBdYgDuY9J9GaL327FdWiEuh6qc_status_chatgpt: "checked_out" -> "done"
{
"type": 16,
"id": "2Mz8tbuiDBMobXfEPcNFhTBUL5fefaAc2nWNcrKZaF1L",
"fee": 500000,
"feeAssetId": null,
"timestamp": 1741268589314,
"version": 2,
"chainId": 84,
"sender": "3NAAoJ554QsZfqE8W8Rg8LsJb79d5b1pDat",
"senderPublicKey": "Ct2djqZsAz77Ur5Y9pMbgrJR2xn6hDprcsHUYgLfsdkY",
"proofs": [
"dqcBE2NBwXudC5EQa1RQABVhkKYDzoQfLxxZKEkhJ7ZiWNvxQvBZXj8Yj3x7zdkCfj6HpyJrX3q4AhsrtQCcv4c"
],
"dApp": "3N9tKixzqTYWnEXQxrDQ5pBTGvQd6sFsvmV",
"payment": [],
"call": {
"function": "commitTask",
"args": [
{
"type": "string",
"value": "6e6wn4Jd9oTMKve4zLbNtye51wt7E6MTDEYT1gkLZQSy_AqqtiUWzxuW2sGQZiUBdYgDuY9J9GaL327FdWiEuh6qc"
},
{
"type": "string",
"value": "Long short-term memory (LSTM) is a type of recurrent neural network architecture developed to address the vanishing gradient problem in traditional RNNs, allowing for capturing long-term dependencies in sequential data. LSTMs utilize a system of gates to regulate the flow of information, enabling better retention and utilization of information over time in the network's memory cells."
}
]
},
"height": 3532229,
"applicationStatus": "succeeded",
"spentComplexity": 67,
"stateChanges": {
"data": [
{
"key": "6e6wn4Jd9oTMKve4zLbNtye51wt7E6MTDEYT1gkLZQSy_AqqtiUWzxuW2sGQZiUBdYgDuY9J9GaL327FdWiEuh6qc_status_chatgpt",
"type": "string",
"value": "done"
},
{
"key": "6e6wn4Jd9oTMKve4zLbNtye51wt7E6MTDEYT1gkLZQSy_AqqtiUWzxuW2sGQZiUBdYgDuY9J9GaL327FdWiEuh6qc_result_chatgpt",
"type": "string",
"value": "Long short-term memory (LSTM) is a type of recurrent neural network architecture developed to address the vanishing gradient problem in traditional RNNs, allowing for capturing long-term dependencies in sequential data. LSTMs utilize a system of gates to regulate the flow of information, enabling better retention and utilization of information over time in the network's memory cells."
},
{
"key": "6e6wn4Jd9oTMKve4zLbNtye51wt7E6MTDEYT1gkLZQSy_AqqtiUWzxuW2sGQZiUBdYgDuY9J9GaL327FdWiEuh6qc_commit_height_chatgpt",
"type": "integer",
"value": 3532229
},
{
"key": "6e6wn4Jd9oTMKve4zLbNtye51wt7E6MTDEYT1gkLZQSy_AqqtiUWzxuW2sGQZiUBdYgDuY9J9GaL327FdWiEuh6qc_commit_timestamp_chatgpt",
"type": "integer",
"value": 1741268514625
},
{
"key": "checked_out_by_Ct2djqZsAz77Ur5Y9pMbgrJR2xn6hDprcsHUYgLfsdkY_chatgpt_6e6wn4Jd9oTMKve4zLbNtye51wt7E6MTDEYT1gkLZQSy_AqqtiUWzxuW2sGQZiUBdYgDuY9J9GaL327FdWiEuh6qc",
"value": null
}
],
"transfers": [],
"issues": [],
"reissues": [],
"burns": [],
"sponsorFees": [],
"leases": [],
"leaseCancels": [],
"invokes": []
}
}
