{
  "id": 270349,
  "title": "Notebook Timeout",
  "url": "/competitions/rsna-miccai-brain-tumor-radiogenomic-classification/discussion/270349",
  "author_name": "",
  "post_date": "2021-09-04T19:20:21.513884700Z",
  "votes": null,
  "comment_count": 2,
  "views": 0,
  "content": "<p>I want to apply pre-train models to each MRI-TYPES and predict each MRI-TYPES.  <br>\nBut after submit,I have got \"Notebook Timeout\"<br>\nSo,I convert test data to TFRecord.<br>\nI can finish submittion,get Public Score.<br>\nSuccessful code is also open to the public, so please refer to it if you like.</p>",
  "messages": [
    {
      "id": "1502951",
      "postDate": "09/04/2021 19:20:21",
      "content": "<p>I want to apply pre-train models to each MRI-TYPES and predict each MRI-TYPES.  <br>\nBut after submit,I have got \"Notebook Timeout\"<br>\nSo,I convert test data to TFRecord.<br>\nI can finish submittion,get Public Score.<br>\nSuccessful code is also open to the public, so please refer to it if you like.</p>",
      "rawMarkdown": "I want to apply pre-train models to each MRI-TYPES and predict each MRI-TYPES.  \nBut after submit,I have got \"Notebook Timeout\"\nSo,I convert test data to TFRecord.\nI can finish submittion,get Public Score.\nSuccessful code is also open to the public, so please refer to it if you like.",
      "votes": null
    },
    {
      "id": "1503063",
      "postDate": "09/04/2021 23:37:37",
      "content": "<p>do you mean you converted the dataset to TFrecord? </p>",
      "rawMarkdown": "do you mean you converted the dataset to TFrecord?",
      "votes": null
    },
    {
      "id": "1503083",
      "postDate": "09/05/2021 01:17:40",
      "content": "<p>Thank you comment.<br>\nThis mean is simple.</p>\n<pre><code>for i in range(4):\n    with tf.io.TFRecordWriter(str(\"./\") + str(\"brain_test_\" + views[i] + \".tfrec\")) as writer:\n        for x in df_preds[\"BraTS21ID\"]:\n            img = data_generation(x,views[i],False)\n            example = serialize_example_test(\n                img)\n            writer.write(example)\n</code></pre>",
      "rawMarkdown": "Thank you comment.\nThis mean is simple.\n\n```\nfor i in range(4):\n    with tf.io.TFRecordWriter(str(\"./\") + str(\"brain_test_\" + views[i] + \".tfrec\")) as writer:\n        for x in df_preds[\"BraTS21ID\"]:\n            img = data_generation(x,views[i],False)\n            example = serialize_example_test(\n                img)\n            writer.write(example)\n```",
      "votes": null
    }
  ],
  "comments": [
    {
      "id": 1503063,
      "author_name": "hawkeat",
      "author_url": "",
      "post_date": "09/04/2021 23:37:37",
      "content": "<p>do you mean you converted the dataset to TFrecord? </p>",
      "votes": null,
      "replies": [
        {
          "id": 1503083,
          "author_name": "hazigin",
          "author_url": "",
          "post_date": "09/05/2021 01:17:40",
          "content": "<p>Thank you comment.<br>\nThis mean is simple.</p>\n<pre><code>for i in range(4):\n    with tf.io.TFRecordWriter(str(\"./\") + str(\"brain_test_\" + views[i] + \".tfrec\")) as writer:\n        for x in df_preds[\"BraTS21ID\"]:\n            img = data_generation(x,views[i],False)\n            example = serialize_example_test(\n                img)\n            writer.write(example)\n</code></pre>",
          "votes": null,
          "replies": []
        }
      ]
    }
  ],
  "raw_markdown_by_id": {
    "1502951": "I want to apply pre-train models to each MRI-TYPES and predict each MRI-TYPES.  \nBut after submit,I have got \"Notebook Timeout\"\nSo,I convert test data to TFRecord.\nI can finish submittion,get Public Score.\nSuccessful code is also open to the public, so please refer to it if you like.",
    "1503063": "do you mean you converted the dataset to TFrecord?",
    "1503083": "Thank you comment.\nThis mean is simple.\n\n```\nfor i in range(4):\n    with tf.io.TFRecordWriter(str(\"./\") + str(\"brain_test_\" + views[i] + \".tfrec\")) as writer:\n        for x in df_preds[\"BraTS21ID\"]:\n            img = data_generation(x,views[i],False)\n            example = serialize_example_test(\n                img)\n            writer.write(example)\n```"
  },
  "source": "meta"
}