{
  "id": 271573,
  "title": "Doubt on Data format",
  "url": "/competitions/rsna-miccai-brain-tumor-radiogenomic-classification/discussion/271573",
  "author_name": "",
  "post_date": "2021-09-11T08:42:08.115565900Z",
  "votes": null,
  "comment_count": 1,
  "views": 0,
  "content": "<p>Hi,<br>\n   Im a new bie to Deep learning and kaggle competition. I'm currently struck while creating a model. The files present in each of the folder \"FLAIR\", \"T1w\" , \"T1wCE\" , \"T2w\" are they Timeseries data or independent. I consider them as continous and created a 3d vector, now the problem the number of files in each folder are different.  How do i generalize and pass them in my Conv3D<br>\nsay in below case <br>\nmodel= Sequential()<br>\nmodel.add(TimeDistributed(cnn, input_shape=(15,224,224,3)))</p>\n<p>15 represents number of files for each folder. as the number is different how can i pass it here. should i split and send 15 images in each folder for processing </p>",
  "messages": [
    {
      "id": "1509407",
      "postDate": "09/11/2021 08:42:08",
      "content": "<p>Hi,<br>\n   Im a new bie to Deep learning and kaggle competition. I'm currently struck while creating a model. The files present in each of the folder \"FLAIR\", \"T1w\" , \"T1wCE\" , \"T2w\" are they Timeseries data or independent. I consider them as continous and created a 3d vector, now the problem the number of files in each folder are different.  How do i generalize and pass them in my Conv3D<br>\nsay in below case <br>\nmodel= Sequential()<br>\nmodel.add(TimeDistributed(cnn, input_shape=(15,224,224,3)))</p>\n<p>15 represents number of files for each folder. as the number is different how can i pass it here. should i split and send 15 images in each folder for processing </p>",
      "rawMarkdown": "Hi,\n   Im a new bie to Deep learning and kaggle competition. I'm currently struck while creating a model. The files present in each of the folder \"FLAIR\", \"T1w\" , \"T1wCE\" , \"T2w\" are they Timeseries data or independent. I consider them as continous and created a 3d vector, now the problem the number of files in each folder are different.  How do i generalize and pass them in my Conv3D\nsay in below case \nmodel= Sequential()\nmodel.add(TimeDistributed(cnn, input_shape=(15,224,224,3)))\n\n15 represents number of files for each folder. as the number is different how can i pass it here. should i split and send 15 images in each folder for processing",
      "votes": null
    },
    {
      "id": "1509818",
      "postDate": "09/11/2021 18:11:06",
      "content": "<p>You can choose the amount you want from the middle, or you can process them after, according to the sample.</p>",
      "rawMarkdown": "You can choose the amount you want from the middle, or you can process them after, according to the sample.",
      "votes": null
    }
  ],
  "comments": [
    {
      "id": 1509818,
      "author_name": "zaakciiru",
      "author_url": "",
      "post_date": "09/11/2021 18:11:06",
      "content": "<p>You can choose the amount you want from the middle, or you can process them after, according to the sample.</p>",
      "votes": null,
      "replies": []
    }
  ],
  "raw_markdown_by_id": {
    "1509407": "Hi,\n   Im a new bie to Deep learning and kaggle competition. I'm currently struck while creating a model. The files present in each of the folder \"FLAIR\", \"T1w\" , \"T1wCE\" , \"T2w\" are they Timeseries data or independent. I consider them as continous and created a 3d vector, now the problem the number of files in each folder are different.  How do i generalize and pass them in my Conv3D\nsay in below case \nmodel= Sequential()\nmodel.add(TimeDistributed(cnn, input_shape=(15,224,224,3)))\n\n15 represents number of files for each folder. as the number is different how can i pass it here. should i split and send 15 images in each folder for processing",
    "1509818": "You can choose the amount you want from the middle, or you can process them after, according to the sample."
  },
  "source": "meta"
}