I'm training a text classification model where the input data consists of term frequency—inverse document hit. My output are possible categories.
Each piece of data has 3 categories, so there are 3 ones in an array of zeros one-hot-encodings.
After 5 epochs, the loss is at 0. I guess this is wrong, forr there are a lot of 0s in my labels, which it classifies correctly.
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Which one of those would be right for my situation large one-hot-encodings with multiple 1s? What do these scores tell me?
In short: In other words, while your first compilation option. Why is that? In the MNIST example, after training, scoring, and predicting the test set as I show above, the two metrics now are the same, as they should be:.
How to One Hot Encode Categorical Variables of a Large Dataset in Python? – Let the Machines Learn
Big one-hot-encoding: Each piece of data has 3 categories, so there are 3 ones in an array of zeros one-hot-encodings My model looks like this: These are the model. Tutanchamunon Tutanchamunon 23 8.Housewives Wants Hot Sex Alpoca
For whoever reviews this post: Just curious, why you are using 'sigmoid' instead of the preferred 'softmax' activation in the last layer? In long: In other words, while your first compilation option model.
Keras reported accuracy: Python version 3. Thanks a lot for this answer! Is that right?Adult Wants Real Sex IN Frankton 46044
So, you can give it a try with the remedy I have Big one for hot above and see what you get and BTW, you could also accept the answer - thanks. Sign up or log in Sign up using Google.
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