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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 ...
Elise Le's user avatar
2 votes
1 answer
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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 ...
Josh's user avatar
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1 vote
0 answers
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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 ...
James Ko's user avatar
  • 175
1 vote
1 answer
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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 ...
rrz0's user avatar
  • 290
26 votes
2 answers
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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: ...
carboncomputed's user avatar
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 ...
Cypher's user avatar
  • 515