Row 25918

Row ID: 25918 | Dataset Entry | Axioma AXP Content Repository

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Again, just guessing, but..

If you look into the guts of the CNN model definition, you'll see that each layer is more than likely some combination of the following:

"Conv2d" takes any size, but the output size changes.

"MaxPool2d" takes any size but its output also changes appropriately

Conv2d and MaxPool2d are algorithmic and only have fixed parameters.

--

"Linear" is for a fully connected layer and is a 'fixed' input and a 'fixed' output size.

Linear is where your model weights go. This is why the sizes for your Linear layer are fixed to whatever the model was trained with.

I believe that if the image size is unbounded, your 'forward' function will fail since the conv/pool final output wont match the first fully connected layer input.

If you can infer with any sized image, then Something Somewhere is adjusting the image size.

But without code, we will never know.

FieldValue
text Again, just guessing, but.. If you look into the guts of the CNN model definition, you'll see that each layer is more than likely some combination of the following: "Conv2d" takes any size, but the output size changes. "MaxPool2d" takes any size but its output also changes appropriately Conv2d and MaxPool2d are algorithmic and only have fixed parameters. -- "Linear" is for a fully connected layer and is a 'fixed' input and a 'fixed' output size. Linear is where your model weights go. T…
label r/deeplearning
dataType comment
communityName r/deeplearning
datetime 2024-05-21
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{
  "text": "Again, just guessing, but..\n\nIf you look into the guts of the CNN model definition, you'll see that each layer is more than likely some combination of the following:\n\n\"Conv2d\" takes any size, but the output size changes.\n\n\"MaxPool2d\" takes any size but its output also changes appropriately\n\nConv2d and MaxPool2d are algorithmic and only have fixed parameters.\n\n--\n\n\"Linear\" is for a fully connected layer and is a 'fixed' input and a 'fixed' output size.  \n\nLinear is where your model weights go.  This is why the sizes for your Linear layer are fixed to whatever the model was trained with.   \n\n\nI believe that if the image size is unbounded, your 'forward' function will fail since the conv/pool final output wont match the first fully connected layer input.\n\nIf you can infer with any sized image, then Something Somewhere is adjusting the image size.\n\n \n\nBut without code, we will never know.",
  "label": "r/deeplearning",
  "dataType": "comment",
  "communityName": "r/deeplearning",
  "datetime": "2024-05-21",
  "username_encoded": "Z0FBQUFBQm5Lak1EUUNxTXdHcVhFMVNTRTJwZjBfc2JreWpzblMzbllGUjQtRWsxQ3lmalo0TEQ2Q1d3bE81NExZUzJnVEhpdjl0RWxkRmJyV0k3UHdpZVVJd2tuZV9hVkE9PQ==",
  "url_encoded": "Z0FBQUFBQm5Lak9TZUU3LUVXNW1tR1AyZVdwTWRXRU93NHpnTGg2TWJtRm1VaUZvMXRVbjJiSk5SQkt2TmFmNm1iSmlCUWhRbHZnYmVXb2pqbE16OFZmV1Q4bTh4MzhKUFowUzVHSXRJbHZFSXVqU3BzMGFkWTdBRkdnR1lDU3pQaFhOcE9UYUNWcXJUUUhucmxGY2JxVUVLRFBVUnRBV09QT1JiY09pdktwRVJTellWTnVobXVSZmlGNVJreU5mVWJFclBpbldEbTla"
}
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