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CNN training accuracy flatlines

the performance of the model depends on the data, but I see a few snags in your approach you might try to change: normalization - you didn't use any normalization. You might try to interpose ...
Tomasz Witkowski's user avatar
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Why Relu is correct for CNN?

Thanks for your answer. I watched this nice guy to understand the concept of CNN and its backprop. I watched more carefully this - video and found out the answer to my problem. He said that there is a ...
Tima's user avatar
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Why Relu is correct for CNN?

I think you're confused about a lot of concepts here. First an empirical example: ...
Karl's user avatar
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1 vote

Why Relu is correct for CNN?

Welcome to the DataScience stack exchange. ReLu is not "correct" or "incorrect" but it is just one of several popular choices for a nonlinearity in neural networks. It sounds like ...
bogovicj's user avatar
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2 votes

What may cause the CNN layer weight regularizer to reduce the model accuracy

In general, I stumbled across voices in literature that we shouldn't use dropout with such a big parameter for shallow networks, as it can violate their capabilities. Example: Piotrowski, A. P., ...
Tomasz Witkowski's user avatar
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How do I get my Neural network to ignore certain values?

My experience - I struggled with a similar issue some time ago in a different problem, where I wanted my network to ignore part of the input data. I tried a few approaches: setting a constant small ...
Tomasz Witkowski's user avatar
1 vote

Converting a Standard LSTM RNN over to a Transformer Model

With a bit of elastic net, dynamic gradient clipping and adjustments to the transformer model build training is progressing nicely now. Here is the build that fixed it: ...
Ted Wilmont's user avatar

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