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Showing posts with the label Softmax
Should I Use Softmax As Output When Using Cross Entropy Loss In Pytorch?

Should I Use Softmax As Output When Using Cross Entropy Loss In Pytorch?

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I have a problem with classifying fully connected deep neural net with 2 hidden layers for MNIST da… Read more Should I Use Softmax As Output When Using Cross Entropy Loss In Pytorch?
Pytorch Equivalent To Tf.nn.softmax_cross_entropy_with_logits And Tf.nn.sigmoid_cross_entropy_with_logits

Pytorch Equivalent To Tf.nn.softmax_cross_entropy_with_logits And Tf.nn.sigmoid_cross_entropy_with_logits

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I found the post here. Here, we try to find an equivalence of tf.nn.softmax_cross_entropy_with_logi… Read more Pytorch Equivalent To Tf.nn.softmax_cross_entropy_with_logits And Tf.nn.sigmoid_cross_entropy_with_logits
Runtimewarning: Invalid Value Encountered In Greater

Runtimewarning: Invalid Value Encountered In Greater

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I tried to implement soft-max with the following code (out_vec is a numpy vector of floats): numera… Read more Runtimewarning: Invalid Value Encountered In Greater
How To Use Softmax Output In Python For Neural-network And Machine-learning To Interpret Multinomial Logit Model?

How To Use Softmax Output In Python For Neural-network And Machine-learning To Interpret Multinomial Logit Model?

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It involves the use of softmax function output for machine-learning and neural-network to understan… Read more How To Use Softmax Output In Python For Neural-network And Machine-learning To Interpret Multinomial Logit Model?