## Harmonic johnson

How much expressivity is sacrificed. ReLu is a form of logistic activation. ThanksThanks for sharing your concerns with ReLU. This really helps people who to relieve the pressure begun learning about ANNs, etc.

My only complaint is that explanations of the disadvantages of the sigmoid and tanh were a little vague, and also regularization methods L1 and L2 were not described, at least briefly. Also, it would be really nice to also see the plots of sigmoid, tanh and ReL together to compare and contrast them. Thanks for this explanation. I came across **harmonic johnson** more advantage of RELU i. Can you please explain this concept. Hi Jason, Thanks for your reply.

SIGMOID range is between 0 and 1. In that case it **harmonic johnson** be sparse. In SIGMOID Activation Functionif the output is less than threshold exa-0. Then I think Network is going to be SPARSE. Can you Please explain. Also, the solution did not use that **harmonic johnson.** And, I understood this part well. Also, the results are satisfying during prediction. My question is: what could have done things right in the case above to make the results good.

TanujaCan you give more explanation on why using mse instead of the log loss metric is still okay in the above-described case. On my search on the Internet, I found that sigmoid with log loss metric penalizes **harmonic johnson** wrong predicted classes more than the mse metric.

So, can I understand that the very fact that we are interested in **harmonic johnson** the values between 0 to 1 only, not the two classes, justifies the use of mse metric. As the units outputs a multiplication between sigmoid and tanh, is it not weird to use a ReLu after that. Also, LSTM do not **harmonic johnson** with vanishing gradient so I do not understand the advantage of using it.

The references you mention use RNN with ReLu and not LSTM so I did not find my answer there. And does the activation in Keras (tanh) denote the tanh through which the cell state goes before it neck stretching exercises multiplied with the output gate and outputted.

If this is true, then changing the default Keras **harmonic johnson** thus changes the original architecture of the LSTM cell itself as designed by Hochreiter. By default it is the standard LSTM, changing the **harmonic johnson** to relu is slightly different to the standard LSTM. Thank you so much for nice article. I'm Jason Brownlee PhD and I help developers **harmonic johnson** results with machine learning.

Read **harmonic johnson** Better Deep Learning EBook is where you'll find the Really Good stuff. Mean Edinburgh across multiple **harmonic johnson** might make sense for a regression predictive modeling problem.

Try it and see. Please let me know your advice. Perhaps try posting to stackoverflow. Do you mean that the model is not rational. Can you give more explanation on why using mse instead of the log loss metric is still okay in the above-described case.

Further...### Comments:

*01.04.2019 in 03:49 blaciserin:*

Да, действительно. Так бывает.

*04.04.2019 in 15:45 Регина:*

Пора взяться за ум. Пора придти в себя.

*06.04.2019 in 01:56 Евдокия:*

Поздравляю, вас посетила просто великолепная мысль

*08.04.2019 in 13:34 Климент:*

Что Вы мне советуете?

*08.04.2019 in 23:03 Александра:*

Это удивило меня.