Row 3801

Row ID: 3801 | Dataset Entry | Axioma AXP Content Repository

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I've recently been developing a deep learning library to get a better understanding of machine learning concepts, right now I'm implementing a soft max activation function, everything seems to be right as i checked the values of the outputs and derivatives on existing softmax calculators and they matxhed up, but when i put my network through training it doesnt learn anything, the outputs are either [0.5, 0.4] ish or they just beome 0.9 randomly with no regard for the target.

When i have multiple samples in my data set like an image classifier all the outputs from seperate samples add up to 1 which is confusing. I know that softmax should distribute the outputs of 1 sample into a "probability" but i dont understand why it's happening between the samples of my data set

I'm confused on whether softmax is just a regular activation function or if it's like a special case. I saw a video where they treated the softmax as a layer instead of an activation function which makes me question if a softmax function has different back propogation steps compared to other activation functions. When i change the last activation function into sigmoid, tanH, or ReLU everything works fine.

Any feedback or comments are greatly appreciated seeing as I've spent hours stuck on this.

FieldValue
text I've recently been developing a deep learning library to get a better understanding of machine learning concepts, right now I'm implementing a soft max activation function, everything seems to be right as i checked the values of the outputs and derivatives on existing softmax calculators and they matxhed up, but when i put my network through training it doesnt learn anything, the outputs are either [0.5, 0.4] ish or they just beome 0.9 randomly with no regard for the target. When i have multip…
label r/neuralnetworks
dataType post
communityName r/neuralnetworks
datetime 2024-03-24
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{
  "text": "\nI've recently been developing a deep learning library to get a better understanding of machine learning concepts, right now I'm implementing a soft max activation function, everything seems to be right as i checked the values of the outputs and derivatives on existing softmax calculators and they matxhed up, but when i put my network through training it doesnt learn anything, the outputs are either [0.5, 0.4] ish or they just beome 0.9 randomly with no regard for the target. \n\nWhen i have multiple samples in my data set like an image classifier all the outputs from seperate samples add up to 1 which is confusing. I know that softmax should distribute the outputs of 1 sample into a \"probability\" but i dont understand why it's happening between the samples of my data set\n\nI'm confused on whether softmax is just a regular activation function or if it's like a special case. I saw a video where they treated the softmax as a layer instead of an activation function which makes me question if a softmax function has different back propogation steps compared to other activation functions. When i change the last activation function into sigmoid, tanH, or ReLU everything works fine.\n\nAny feedback or comments are greatly appreciated seeing as I've spent hours stuck on this.\n",
  "label": "r/neuralnetworks",
  "dataType": "post",
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  "datetime": "2024-03-24",
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