All Questions
6 questions
0
votes
1
answer
1k
views
Single input - multiple outputs with different loss functions in Keras: how is the gradient computed?
I've implemented a neural network with single input - multiple outputs using Keras API. The general structure of the network is like in this figure:
Because each branch does a different task, I ...
2
votes
1
answer
1k
views
Numerical computation of cross entropy in practice
The equation for cross-entropy is:
$H(p,q)=-\sum_x{p(x)\log{q(x)}}$
When working with a binary classification problem, the ground truth is often provided to us as binary (i.e. 1's and 0's).
If I ...
1
vote
0
answers
642
views
Need help writing a neural network for a Pokemon battle
I'm trying to write a neural network that's able to select the optimal course of action in a Pokemon battle. In a battle, there are two different types of actions: use one of the four moves known by ...
1
vote
1
answer
1k
views
How does backpropagation differ from reverse-mode autodiff
Going through this book, I am familiar with the following:
For each training instance the backpropagation algorithm first makes a
prediction (forward pass), measures the error, then goes through ...
26
votes
2
answers
25k
views
How does minibatch gradient descent update the weights for each example in a batch?
If we process say 10 examples in a batch, I understand we can sum the loss for each example, but how does backpropagation work in regard to updating the weights for each example?
For example:
...
3
votes
1
answer
2k
views
Explaining the distributions in Tensorflow's Tensorboard
I'm trying to train a cGAN network on Tensorflow and have all the summaries of the Discriminator, but I'm having difficulty understanding what they mean...
There are currently 5 layers in the ...